| { | |
| "paper_id": "2022", | |
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| "date_generated": "2023-01-19T13:55:16.271056Z" | |
| }, | |
| "title": "Keynote Talk: Dialog Management for Conversational Task-Oriented Industry Solutions", | |
| "authors": [ | |
| { | |
| "first": "Maria-Georgia", | |
| "middle": [], | |
| "last": "Zachari Omilia", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Zhou", | |
| "middle": [], | |
| "last": "Yu", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Michael", | |
| "middle": [], | |
| "last": "Tjalve", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Gokhan", | |
| "middle": [], | |
| "last": "Tur", | |
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| "abstract": "This talk will focus on how the Omilia Cloud Platform\u00ae leverages the notion of Dialog Act in order to solve real-life use cases in task-oriented dialog systems for call centers. We will address the challenge of completing tasks efficiently, achieving high KPIs and integrating with a call center, while at the same time building and maintaining a flexible conversational NLU system.", | |
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| "abstract": [ | |
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| "text": "This talk will focus on how the Omilia Cloud Platform\u00ae leverages the notion of Dialog Act in order to solve real-life use cases in task-oriented dialog systems for call centers. We will address the challenge of completing tasks efficiently, achieving high KPIs and integrating with a call center, while at the same time building and maintaining a flexible conversational NLU system.", | |
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| "section": "Abstract", | |
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| { | |
| "text": "Welcome to the 4th Workshop on NLP for Conversational AI, at ACL 2022.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
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| { | |
| "text": "Ever since the invention of the intelligent machine, hundreds and thousands of mathematicians, linguists, and computer scientists have dedicated their careers to empowering human-machine communication in natural language. Although the idea is finally around the corner with a proliferation of virtual personal assistants such as Siri, Alexa, Google Assistant, and Cortana, the development of these conversational agents remains difficult and there still remain plenty of unanswered questions and challenges.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
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| "text": "Conversational AI is hard because it is an interdisciplinary subject. Initiatives were started in different research communities, from Dialogue State Tracking Challenges to NeurIPS Conversational Intelligence Challenge live competition and the Amazon Alexa Prize. However, various fields within the NLP community, such as semantic parsing, coreference resolution, sentiment analysis, question answering, and machine reading comprehension etc. have been seldom evaluated or applied in the context of conversational AI.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
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| "text": "The goal of this workshop is to bring together NLP researchers and practitioners in different fields, alongside experts in speech and machine learning, to discuss the current state-of-the-art and new approaches, to share insights and challenges, to bridge the gap between academic research and real-world product deployment, and to shed light on future directions. \"NLP for Conversational AI\" will be a one-day workshop including keynotes, spotlight talks, and poster sessions. In keynote talks, senior technical leaders from industry and academia will share insights on the latest developments in the field.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
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| "text": "An open call for papers will be announced to encourage researchers and students to share their prospects and latest discoveries. The panel discussion will focus on the challenges, future directions of conversational AI research, bridging the gap in research and industrial practice, as well as audience suggested topics.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
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| { | |
| "text": "With the increasing trend of conversational AI, NLP4ConvAI 2022 is competitive. We received 45 submissions directly to the workshop and 14 submissions through the ACL Rolling Review. After a rigorous review process, we only accepted 18 papers. There are 15 long papers and 3 short papers. The workshop overall acceptance rate is about 30.5%.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
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| "text": "We hope you will enjoy NLP4ConvAI 2022 at ACL and contribute to the future success of our community! Abstract: A pressing challenge in current dialogue systems is to successfully converse with users on topics with information distributed across different modalities. Previous work in multi-turn dialogue systems has primarily focused on either text or table information. In more realistic scenarios, having a joint understanding of both is critical as knowledge is typically distributed over both unstructured and structured forms. In this talk, I will present a new dialogue dataset, HybriDialogue, which consists of crowdsourced natural conversations grounded on both Wikipedia text and tables. The conversations are created through the decomposition of complex multihop questions into simple, realistic multiturn dialogue interactions. We conduct several baseline experiments, including retrieval, system state tracking, and dialogue response generation. Our results show that there is still ample opportunity for improvement, demonstrating the importance of building stronger dialogue systems that can reason over the complex setting of information-seeking dialogue grounded on tables and text. I will also briefly mention a few related studies on dialogue research from the UCSB NLP Group.", | |
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| "text": "Abstract: Recent advances in deep learning based methods for language processing, especially using self-supervised learning methods resulted in new excitement towards building more sophisticated Conversational AI systems. While this is partially true for social chatbots or retrieval-based applications, it is commonplace to see dialogue processing as yet another task while assessing these new state of the art approaches. In this talk, I will argue that Conversational AI comes with an orthogonal methodology for machine learning to complement such methods interacting with the users using implicit and explicit signals. This is an exceptional opportunity for Conversational AI research moving forward and I will present couple representative efforts from Alexa AI.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
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