{ "paper_id": "J02-4007", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T02:44:10.966838Z" }, "title": "Squibs and Discussions Human Variation and Lexical Choice", "authors": [ { "first": "Ehud", "middle": [], "last": "Reiter", "suffix": "", "affiliation": { "laboratory": "", "institution": "University of Aberdeen", "location": { "postCode": "AB24 3UE", "settlement": "Aberdeen", "country": "UK" } }, "email": "ereiter@csd.abdn.ac.uk" }, { "first": "Somayajulu", "middle": [], "last": "Sripada", "suffix": "", "affiliation": { "laboratory": "", "institution": "University of Aberdeen", "location": { "postCode": "AB24 3UE", "settlement": "Aberdeen", "country": "UK" } }, "email": "ssripada@csd.abdn.ac.uk" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "Much natural language processing research implicitly assumes that word meanings are fixed in a language community, but in fact there is good evidence that different people probably associate slightly different meanings with words. We summarize some evidence for this claim from the literature and from an ongoing research project, and discuss its implications for natural language generation, especially for lexical choice, that is, choosing appropriate words for a generated text.", "pdf_parse": { "paper_id": "J02-4007", "_pdf_hash": "", "abstract": [ { "text": "Much natural language processing research implicitly assumes that word meanings are fixed in a language community, but in fact there is good evidence that different people probably associate slightly different meanings with words. We summarize some evidence for this claim from the literature and from an ongoing research project, and discuss its implications for natural language generation, especially for lexical choice, that is, choosing appropriate words for a generated text.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "A major task in natural language generation (NLG) is lexical choice, that is, choosing lexemes (words) to communicate to the reader the information selected by the system's content determination module. From a semantic perspective lexical choice algorithms are based on models of word meanings, which state when a word can and cannot be used; of course, lexical choice algorithms may also consider syntactic constraints and pragmatic features when choosing words.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "Such models assume that it is possible to specify what a particular word means to a particular user. However, both the cognitive science literature and recent experiments carried out in the SUMTIME project at the University of Aberdeen, of which the current authors are a part, suggest that this may be difficult to do because of variations among people, that is, because the same word may mean different things to different people. More precisely, although people may agree at a rough level about what a word means, they may disagree about its precise definition, and in particular, to what objects or events a word can be applied. This means that it may be impossible even in principle to specify precise word meanings for texts with multiple readers, and indeed for texts with a single reader, unless the system has access to an extremely detailed user model. A corpus study in our project also showed that there were differences in which words individuals used (in the sense that some words were used only by a subset of the authors) and also in how words were orthographically realized (spelled).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "This suggests that it may be risky for NLG systems (and indeed human authors) to depend for communicative success on the human reader's interpreting words exactly as the system intends. This in turn suggests that perhaps NLG systems should be cautious in using very detailed lexical models and also that it may be useful to add some redundancy to texts in case the reader does not interpret a word as expected. This is especially true in applications in which each user reads only one generated text; if users read many generated texts, then perhaps over time they will learn about and adapt to the NLG system's lexical usage. Human variability also needs to be taken into account by natural language processing (NLP) researchers performing corpora analyses; such analyses should not assume that everyone uses identical rules when making linguistic decisions.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "Linguists have acknowledged that people may associate different meanings with the same word. Nunberg (1978, page 81) , for example, writes:", "cite_spans": [ { "start": 93, "end": 116, "text": "Nunberg (1978, page 81)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Previous Research", "sec_num": "2.1" }, { "text": "There is considerable variation among speakers in beliefs about what does and does not constitute a member of the category. . . . Take jazz. I may believe that the category includes ragtime, but not blues; you may believe the exact opposite. After all, we will have been exposed to a very different set of exemplars. And absent a commonly accepted authority, we must construct our own theories of categories, most probably in the light of varying degrees of musical sophistication.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Previous Research", "sec_num": "2.1" }, { "text": "Many modern theories of mental categorization (Rosch 1978; Smith and Medin 1981) assume that mental categories are represented by prototypes or exemplars. Therefore, if different people are exposed to different category prototypes and exemplars, they are likely to have different rules for evaluating category membership. Parikh (1994) made a similar point and backed it up with some simple experimentation. For example, he showed squares from the Munsell chart to participants and asked them to characterize the squares as red or blue; different individuals characterized the squares in different ways. In another experiment he showed that differences remained even if participants were allowed to associate fuzzy-logic-type truth values with statements.", "cite_spans": [ { "start": 46, "end": 58, "text": "(Rosch 1978;", "ref_id": "BIBREF14" }, { "start": 59, "end": 80, "text": "Smith and Medin 1981)", "ref_id": "BIBREF15" }, { "start": 322, "end": 335, "text": "Parikh (1994)", "ref_id": "BIBREF10" } ], "ref_spans": [], "eq_spans": [], "section": "Previous Research", "sec_num": "2.1" }, { "text": "In the psychological community, Malt et al. (1999) investigated what names participants gave to real-world objects. For example, these researchers wished to know whether participants would describe a pump-top hand lotion dispenser as a bottle or a container. They were primarily interested in variations across linguistic communities, but they also discovered that even within a linguistic community there were differences in how participants named objects. They state (page 242) that only 2 of the 60 objects in their study were given the same name by all of their 76 native-Englishspeaker participants.", "cite_spans": [ { "start": 32, "end": 50, "text": "Malt et al. (1999)", "ref_id": "BIBREF7" } ], "ref_spans": [], "eq_spans": [], "section": "Previous Research", "sec_num": "2.1" }, { "text": "In the lexicographic community, field-workers for the Dictionary of American Regional English (DARE) (Cassidy and Hall 1996) asked a representative set of Americans to respond to fill-in-the-blank questionnaires. The responses they received revealed substantial differences among participants. For example, there were 228 different responses to question B12, When the wind begins to increase, you say it's , the most common of which were getting windy and blowing up; and 201 different responses to question B13, When the wind begins to decrease, you say it's , the most common of which were calming down and dying down.", "cite_spans": [ { "start": 101, "end": 124, "text": "(Cassidy and Hall 1996)", "ref_id": "BIBREF1" } ], "ref_spans": [], "eq_spans": [], "section": "Previous Research", "sec_num": "2.1" }, { "text": "The SUMTIME project at the University of Aberdeen is researching techniques for generating summaries of time-series data. 1 Much of the project focuses on content determination (see, for example, Sripada et al. [2001] ), but it is also examining lexical choice algorithms for time-series summaries, which is where the work described in this article originated. To date, SUMTIME has primarily focused on two domains, weather forecasts and summaries of gas turbine sensors, although we have recently started work in a third domain as well, summaries of sensor readings in neonatal intensive care units.", "cite_spans": [ { "start": 196, "end": 217, "text": "Sripada et al. [2001]", "ref_id": "BIBREF16" } ], "ref_spans": [], "eq_spans": [], "section": "SUMTIME Project", "sec_num": "2.2" }, { "text": "In order to develop a lexicon for describing patterns in gas turbine sensor data, we asked two experts to write short descriptions of 38 signal fragments. (A signal fragment is an interval of a single time-series data channel.) The descriptors were small, with an average size of 8.3 words. In no case did the experts produce exactly the same descriptor for a fragment. Many of the differences simply reflected usage of different words to express the same underlying concept; for example, one expert typically used rise to describe what the other expert called increase. In other cases the differences reflected different levels of detail. For example, one fragment was described by expert A as Generally steady with a slow rising trend. Lots of noise and a few small noise steps, whereas expert B used the shorter phrase Rising trend, with noise. Both experts also had personal vocabulary; for example, the terms bathtub and dome were used only by expert A, whereas the terms square wave and transient were used only by expert B.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Gas Turbine Domain.", "sec_num": "2.2.1" }, { "text": "Most importantly from the perspective of this article, there were cases in which the differences between the experts reflected a difference in the meanings associated with words. For example, both experts used the word oscillation. Six signals were described by both experts as oscillations, but two signals, including the one shown in Figure 1 , were described as oscillations only by expert B. We do not have enough examples to solidly support hypotheses about why the experts agreed on application of the term oscillation to some signals and disagreed on its application to others, but one explanation that seems to fit the available data is that the experts agreed on applying the term to signals that were very similar to a sine wave (which presumably is the prototype [Rosch 1978 ] of an oscillation), but sometimes disagreed on its application to signals that were less similar to a sine wave, such as Figure 1.", "cite_spans": [ { "start": 774, "end": 785, "text": "[Rosch 1978", "ref_id": "BIBREF14" } ], "ref_spans": [ { "start": 336, "end": 344, "text": "Figure 1", "ref_id": "FIGREF0" } ], "eq_spans": [], "section": "Gas Turbine Domain.", "sec_num": "2.2.1" }, { "text": "In the meteorology domain we accumulated and analyzed a corpus of 1,099 human-written weather forecasts for offshore oil rigs, together with the data files (produced by a numerical weather simulation) that the forecasters examined when writing the forecasts. The forecasts were written by five different forecasters. A short extract from a typical forecast is shown in Figure 2 ; this text describes changes in wind speed and direction predicted to occur two days after the forecast was issued. An extract from the corresponding data file is shown in Table 1 ; it describes the predicted wind speed and direction from the numerical weather simulation at three-hourly intervals.", "cite_spans": [], "ref_spans": [ { "start": 369, "end": 377, "text": "Figure 2", "ref_id": null }, { "start": 551, "end": 558, "text": "Table 1", "ref_id": "TABREF0" } ], "eq_spans": [], "section": "Meteorology Domain.", "sec_num": "2.2.2" }, { "text": "As in the gas turbine domain, our corpus analysis showed that individual forecasters had idiosyncratic vocabulary that only they used. For example, one forecaster used the verb freshening to indicate a moderate increase in wind speed from a low or moderate initial value, but no other forecaster used this verb. There were also FORECAST 00-24 GMT, WEDNESDAY, 04-Oct 2000 WIND(10M): WSW 20-24 BACKING SSW 10-14 BY MIDDAY THEN VEERING SW 24-28 BY EVENING", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Meteorology Domain.", "sec_num": "2.2.2" }, { "text": "Wind (at 10m) extract from five-day weather forecast issued on October 2, 2000.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Figure 2", "sec_num": null }, { "text": "differences in orthography. For example, some forecasters lexicalized the four basic directions as N, E, S, and W, whereas others used the lexicalizations N'LY, E'LY, S'LY, and W'LY.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Figure 2", "sec_num": null }, { "text": "We performed a number of semantic analyses to determine when different forecasters used different words; these invariably showed differences between authors. For example, we attempted to infer the meaning of time phrases such as by evening by searching for the first data file record that matched the corresponding wind descriptor. The forecast in Figure 2 , for example, says that the wind will change to SSW 10-14 at the time suggested by BY MIDDAY. In the corresponding data shown in Table 1 , the first entry with a direction of SSW and a speed in the 10-14 range is 1200; hence in this example the time phrase by midday is associated with the time 1200. A similar analysis suggests that in this example the time phrase by evening is associated with the time 0000 (on 5-10-00).", "cite_spans": [], "ref_spans": [ { "start": 348, "end": 356, "text": "Figure 2", "ref_id": null }, { "start": 487, "end": 494, "text": "Table 1", "ref_id": "TABREF0" } ], "eq_spans": [], "section": "Figure 2", "sec_num": null }, { "text": "We repeated this procedure for every forecast in our corpus and statistically analyzed the results to determine how individual forecasters used time phrases. More details about the analysis procedure are given by Reiter and Sripada (2002) . As reported in that paper, the forecasters seemed to agree on the meaning of some time phrases; for example, all forecasters predominantly used by midday to mean 1200. They disagreed, however, on the use of other terms, including by evening. The use of by evening is shown in Table 2; in particular, whereas forecaster F3 (the author of the text in Figure 2 ) most often used this phrase to mean 0000, forecasters F1 and F4 most often used this phrase to mean 1800. The differences between forecasters in their usage of by evening are significant at p < .001 under both a chi-squared test (which treats time as a categorical variable) and a one-way analysis of variance (which compares the mean time for each forecaster; for this test we recoded the hour 0 as 24).", "cite_spans": [ { "start": 213, "end": 238, "text": "Reiter and Sripada (2002)", "ref_id": "BIBREF13" } ], "ref_spans": [ { "start": 590, "end": 598, "text": "Figure 2", "ref_id": null } ], "eq_spans": [], "section": "Figure 2", "sec_num": null }, { "text": ". Some colleagues pointed out to us that meteorology in particular was a domain with an established sublanguage and usage conventions, whose words might correspond to technical terms, and wondered what would hap- pen in a domain in which there was no established sublanguage and technical terminology. We therefore performed a small experiment at the University of Aberdeen in which we asked 21 postgraduate students and academic staff members to fill out a questionnaire asking which of the following individuals they would regard as knowing Java: 2", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Knows Java Experiment", "sec_num": "2.2.3" }, { "text": "\u2022 A cannot program in Java, but knows that Java is a popular programming language.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Knows Java Experiment", "sec_num": "2.2.3" }, { "text": "\u2022 B cannot write a Java program from scratch, but can make very simple changes to an existing Java program (such as changing a string constant that specifies a URL).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Knows Java Experiment", "sec_num": "2.2.3" }, { "text": "\u2022 C can use a tool such as JBuilder to write a very simple Java program, but cannot use control flow constructs such as while loops.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Knows Java Experiment", "sec_num": "2.2.3" }, { "text": "\u2022 D can write Java programs that use while loops, arrays, and the Java class libraries, but only within one class; she cannot write a program that consists of several classes.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Knows Java Experiment", "sec_num": "2.2.3" }, { "text": "\u2022 E can create complex Java programs and classes, but needs to occasionally refer to documentation for details of the Java language and class libraries.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Knows Java Experiment", "sec_num": "2.2.3" }, { "text": "Respondents could tick Yes, Unsure, or No. All 21 respondents ticked No for A and Yes for E. They disagreed about whether B, C, and D could be considered to know Java; 3 ticked Yes for B, 5 ticked Yes for C, and 13 ticked Yes for D. In other words, even among this relatively homogeneous group, there was considerable disagreement over what the phrase knows Java meant in terms of actual knowledge of the Java programming language.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Knows Java Experiment", "sec_num": "2.2.3" }, { "text": "The previous section has argued that people in many cases do associate different meanings with lexemes and phrases such as oscillation, by evening, and knows; and that some words, such as bathtub and freshening, are used only by a subset of authors in a particular domain. What impact does this have on lexical choice?", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Lexical Choice", "sec_num": "3.1" }, { "text": "In applications in which users read only one generated text, it may be necessary to restrict lexeme definitions to those that we expect all users to share. Indeed, essentially this advice was given to us by a domain expert in an earlier project on generating personalized smoking-cessation letters (Reiter, Robertson, and Osman 2000) . In applications in which users read many generated texts over a period of time, however, an argument could be made for using a richer vocabulary and set of lexeme definitions and expecting users to adapt to and learn the system's vocabulary and usage over the course of time; it may be appropriate to add \"redundant\" information to texts (section 3.2) while the user is still learning the system's lexical usage. This strategy has some risks, but if successful, can lead to short and less awkward texts. Consistency is essential if this strategy is followed; the system should not, for example, sometimes use by evening to mean 1800 and sometimes use by evening to mean 0000.", "cite_spans": [ { "start": 298, "end": 333, "text": "(Reiter, Robertson, and Osman 2000)", "ref_id": "BIBREF12" } ], "ref_spans": [], "eq_spans": [], "section": "Lexical Choice", "sec_num": "3.1" }, { "text": "We are not aware of previous research in lexical choice that focuses on dealing with differences in the meanings that different human readers associate with words. Perhaps the closest research strand is that which investigates tailoring word choice and phrasing according to the expertise of the user (Bateman and Paris 1989; Reiter 1991; McKeown, Robin, and Tanenblatt 1993) . For example, the Coordinated Multimedia Explanation Testbed (COMET) (McKeown, Robin, and Tanenblatt 1993) could generate Check the polarity for skilled users and Make sure the plus on the battery lines up with the plus on the battery compartment for unskilled users. General reviews of previous lexical choice research in NLG are given by Stede (1995) and Wanner (1996) ; Zukerman and Litman (2001) review research in user modeling and NLP.", "cite_spans": [ { "start": 301, "end": 325, "text": "(Bateman and Paris 1989;", "ref_id": "BIBREF0" }, { "start": 326, "end": 338, "text": "Reiter 1991;", "ref_id": "BIBREF11" }, { "start": 339, "end": 375, "text": "McKeown, Robin, and Tanenblatt 1993)", "ref_id": "BIBREF8" }, { "start": 446, "end": 483, "text": "(McKeown, Robin, and Tanenblatt 1993)", "ref_id": "BIBREF8" }, { "start": 717, "end": 729, "text": "Stede (1995)", "ref_id": "BIBREF17" }, { "start": 734, "end": 747, "text": "Wanner (1996)", "ref_id": "BIBREF18" }, { "start": 750, "end": 776, "text": "Zukerman and Litman (2001)", "ref_id": "BIBREF19" } ], "ref_spans": [], "eq_spans": [], "section": "Lexical Choice", "sec_num": "3.1" }, { "text": "In the linguistic community, Parikh (1994) has suggested that utility theory be applied to word choice. In other words, if we know (1) the probability of a word's being correctly interpreted or misinterpreted and (2) the benefit to the user of correct interpretation and the cost of misinterpretation, then we can compute an overall utility to the user of using the word. This seems like an interesting theoretical model, but in practice (at least in the applications we have looked at), whereas it may be just about possible to get data on the likelihood of correct interpretation of a word, it is probably impossible to calculate the cost of misinterpretation, because we do not have accurate task models that specify exactly how the user will use the generated texts (and our domain experts have told us that it is probably impossible to construct such models).", "cite_spans": [ { "start": 29, "end": 42, "text": "Parikh (1994)", "ref_id": "BIBREF10" } ], "ref_spans": [], "eq_spans": [], "section": "Lexical Choice", "sec_num": "3.1" }, { "text": "A related problem is choosing between near synonyms, that is, words with similar meanings: for example, choosing between easing and decreasing when describing changes in wind speed, or saw-tooth transient and shark-tooth transient when describing gas turbine signals. The most in-depth examination of choosing between near synonyms was undertaken by Edmonds (1999) , who essentially suggested using rules based on lexicographic work such as Webster's New Dictionary of Synonyms (Gove 1984).", "cite_spans": [ { "start": 350, "end": 364, "text": "Edmonds (1999)", "ref_id": "BIBREF3" } ], "ref_spans": [], "eq_spans": [], "section": "Near Synonyms.", "sec_num": "3.1.1" }, { "text": "Edmonds was working on machine translation, not generating texts from nonlinguistic data, and hence was looking at larger differences than the ones with which we are concerned. Indeed, dictionaries do not in general give definitions at the level of detail required by SUMTIME; for example, we are not aware of any dictionary that defines oscillation in enough detail to specify whether it is appropriate to describe the signal in Figure 1 as an oscillation. We also have some doubts, however, as to whether the definitions given in synonym dictionaries such as Gove (1984) do indeed accurately represent how all members of a language community use near synonyms. For example, when describing synonyms and near synonyms of error, Gove (page 298) states that faux pas is \"most frequently applied to a mistake in etiquette.\" This seems to be a fair match to DARE's fieldwork question (see section 2.1) JJ41, An embarrassing mistake: Last night she made an awful , and indeed DARE (volume 2, page 372) states that faux pas was a frequent response to this question. DARE adds, however, that faux pas was less often used in the South Midland region of the United States (the states of Kentucky and Tennessee and some adjacent regions of neighboring states) and also was less often used by people who lacked a college education. So although faux pas might be a good lexicalization of a \"mistake in etiquette\" for most Americans, it might not be appropriate for all Americans; and, for example, if an NLG system knew that its user was a non-college-educated man from Kentucky, then perhaps it should consider using another word for this concept.", "cite_spans": [], "ref_spans": [ { "start": 430, "end": 438, "text": "Figure 1", "ref_id": "FIGREF0" } ], "eq_spans": [], "section": "Near Synonyms.", "sec_num": "3.1.1" }, { "text": "Another implication of human variation in word usage is that if there is a chance that people may not interpret words as expected, it may be useful for an NLG system to include extra information in the texts it generates, beyond what is needed if users could be expected to interpret words exactly as the system intended. Indeed, unexpected word interpretations could perhaps be considered to be a type of \"semantic\" noise; and as with all noise, redundancy in the signal (text) can help the reader recover the intended meaning.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Redundancy", "sec_num": "3.2" }, { "text": "For example, the referring-expression generation model of Dale and Reiter (1995) selects attributes to identify referents based on the assumption that the hearer will interpret the attributes as the system expects. Assume, for instance, that there are two books in focus, B 1 and B 2 , and the system's knowledge base records that B 1 has color red and B 2 has color blue. Then, according to Dale and Reiter, the red book is a distinguishing description that uniquely identifies B 1 .", "cite_spans": [ { "start": 58, "end": 80, "text": "Dale and Reiter (1995)", "ref_id": "BIBREF2" } ], "ref_spans": [], "eq_spans": [], "section": "Redundancy", "sec_num": "3.2" }, { "text": "Parikh, however, has shown that people can in fact disagree about which objects are red and which are blue; if this is the case, then it is possible that the hearer will not in fact be able to identify B 1 as the referent after hearing the red book. To guard against this eventuality, it might be useful to add additional information about a different attribute to the referring expression; for example, if B 1 is 100 pages and B 2 is 1,000 pages, the system could generate the thin red book. This is longer than the red book and thus perhaps may take longer to utter and comprehend, but its redundancy provides protection against unexpected lexical interpretations.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Redundancy", "sec_num": "3.2" }, { "text": "A final point is that differences between individuals should perhaps be considered in general by people performing corpus analyses to derive rules for NLG systems (Reiter and Sripada 2002) . To take one randomly chosen example, Hardt and Rambow (2001) suggest a set of rules for deciding on verb phrase (VP) ellipsis that are based on machine learning techniques applied to the Penn Treebank corpus. These achieve a 35% reduction in error rate against a baseline rule. They do not consider variation in author, and we wonder if a considerable amount of the remaining error is due to individual variation in deciding when to ellide a VP. It would be interesting to perform a similar analysis with author specified as one of the features given to the machine learning algorithm and see whether this improved performance.", "cite_spans": [ { "start": 163, "end": 188, "text": "(Reiter and Sripada 2002)", "ref_id": "BIBREF13" }, { "start": 228, "end": 251, "text": "Hardt and Rambow (2001)", "ref_id": "BIBREF6" } ], "ref_spans": [], "eq_spans": [], "section": "Corpus Analysis", "sec_num": "3.3" }, { "text": "To finish on a more positive note, human variation may potentially be an opportunity for NLG systems, because they can guarantee consistency. In the weather-forecasting domain we examined, for example, users receive texts written by all five forecasters, which means that they may have problems reliably interpreting phrases such as by evening; an NLG system, in contrast, could be programmed always to use this phrase consistently. An NLG system could also be programmed to avoid idiosyncratic terms with which users might not be familiar (bathtub, for example) and not to use terms in cases in which people disagree about their applicability (e.g., oscillation for Figure 1 ). Our corpus analyses and discussions with domain experts suggest that it is not always easy for human writers to follow such consistency rules, especially if they have limited amounts of time.", "cite_spans": [], "ref_spans": [ { "start": 667, "end": 675, "text": "Figure 1", "ref_id": "FIGREF0" } ], "eq_spans": [], "section": "Natural Language Generation versus Human-Written Texts", "sec_num": "4." }, { "text": "Psychologists believe that interaction is a key aspect of the process of humans agreeing on word usage (Garrod and Anderson 1987) . Perhaps a small group of people who constantly communicate with each other over a long time period (presumably the circumstances under which language evolved) will agree on word meanings. But in the modern world it is common for human writers to write documents for people whom they have never met or with whom they have never otherwise interacted, which may reduce the effectiveness of the natural interaction mechanism for agreeing on word meanings.", "cite_spans": [ { "start": 103, "end": 129, "text": "(Garrod and Anderson 1987)", "ref_id": "BIBREF4" } ], "ref_spans": [], "eq_spans": [], "section": "Natural Language Generation versus Human-Written Texts", "sec_num": "4." }, { "text": "In summary, dealing with lexical variation among human readers is a challenge for NLG systems and will undoubtably require a considerable amount of thought, research, and data collection. But if NLG systems can do a good job of this, they might end up producing superior texts to many human writers, which would greatly enhance the appeal of NLG technology.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Natural Language Generation versus Human-Written Texts", "sec_num": "4." }, { "text": "See http://www.csd.abdn.ac.uk/research/sumtime for general information about SUMTIME.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "The questionnaire in fact contained a sixth item: \"F can create complex Java libraries and almost never needs to refer to documentation because she has memorised most of it.\" However, a few participants were unsure whether create complex Java libraries meant programming or meant assembling compiled object files into a single archive file using a tool such as tar or jartool, so we dropped F from our study.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null } ], "back_matter": [ { "text": "Our thanks to the many individuals who have discussed this work with us (not all of whom agree with our analysis!), including Regina Barzilay, Ann Copestake, Robert Dale, Phil Edmonds, Jim Hunter, Adam Kilgarriff, Owen Rambow, Graeme Ritchie, Rosemary Stevenson, Sandra Williams, and Jin Yu. We are also grateful to the anonymous reviewers for their helpful comments. Special thanks to DARE editor Joan Hall for providing us with the DARE fieldwork data. Last but certainly not least, this work would not have been possible without the help of our industrial collaborators at Intelligent Applications and WNI/Oceanroutes. This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC), under grant GR/M76681.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Acknowledgments", "sec_num": null } ], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "Phrasing a text in terms the user can understand", "authors": [ { "first": "John", "middle": [], "last": "Bateman", "suffix": "" }, { "first": "Cecile", "middle": [], "last": "Paris", "suffix": "" } ], "year": 1989, "venue": "Proceedings of the 11th International Joint Conference on Artificial Intelligence (IJCAI-89)", "volume": "2", "issue": "", "pages": "1511--1517", "other_ids": {}, "num": null, "urls": [], "raw_text": "Bateman, John and Cecile Paris. 1989. Phrasing a text in terms the user can understand. 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User Modeling and User-Adapted Interaction, 11:129-158.", "links": null } }, "ref_entries": { "FIGREF0": { "text": "Signal fragment (gas turbine exhaust temperature): Is this an oscillation?", "num": null, "uris": null, "type_str": "figure" }, "TABREF0": { "type_str": "table", "text": "Wind (at 10m) extract from October 2, 2000, data file (output of numerical weather model).", "content": "
Day Hour Wind Direction Wind Speed
4-10-000WSW22
4-10-003WSW20
4-10-006SW16
4-10-009SW14
4-10-0012SSW12
4-10-0015SSW18
4-10-0018SSW22
4-10-0021SSW24
5-10-000SW26
", "html": null, "num": null }, "TABREF1": { "type_str": "table", "text": "How often by evening was used to refer to each time, for each forecaster (mode in boldface font).", "content": "
Hour F1 F2 F3 F4 F5 Total
05 351344
311
611
90
1211
1552310
18 1931 22449
2175 223643
Total 31 14 61 29 14149
", "html": null, "num": null } } } }