Semantic Grounding in Dialogue for Complex Problem Solving

Li, Xiaolong; Boyer, Kristy Elizabeth

doi:10.3115/v1/n15-1085

Cited by 4 publications

(6 citation statements)

References 25 publications

(22 reference statements)

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“…• Interactive: In a conversational setting, multitasking is used to compute concept similarity judgements (Silberer and Lapata, 2014), knowledge grounded response generation (Majumder et al, 2020), grounding language instructions Hu et al (2019). Joint modeling is used by Li and Boyer (2015) to address dialog for complex problem solving in computer programs. 3b) Loss Function: It is crucial to utilize appropriate loss designed for the specific grounding task.…”

Section: Learning Objectivementioning

confidence: 99%

“…• Interactive: Coming to an interactive setting, the datasets span tasks like conversations based on negotiations (Cadilhac et al, 2013), referring expressions from images (Haber et al, 2019;Takmaz et al, 2020), emotions and styles (Shuster et al, 2020), media interviews (Majumder et al, 2020), documents (Zhou et al, 2018b), improvisation (Cho and May, 2020), problem solving (Li and Boyer, 2015), spatial reasoning in a simulated environment (Jänner et al, 2018), navigation (Ku et al, 2020) etc.,…”

Section: New Datasetsmentioning

confidence: 99%

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Grounding ‘Grounding’ in NLP

Chandu

Bisk

Black

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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The NLP community has seen substantial recent interest in grounding to facilitate interaction between language technologies and the world. However, as a community, we use the term broadly to reference any linking of text to data or non-textual modality. In contrast, Cognitive Science more formally defines "grounding" as the process of establishing what mutual information is required for successful communication between two interlocutorsa definition which might implicitly capture the NLP usage but differs in intent and scope.We investigate the gap between these definitions and seek answers to the following questions: (1) What aspects of grounding are missing from NLP tasks? Here we present the dimensions of coordination, purviews and constraints.(2) How is the term "grounding" used in the current research? We study the trends in datasets, domains, and tasks introduced in recent NLP conferences. And finally, (3) How to advance our current definition to bridge the gap with Cognitive Science? We present ways to both create new tasks or repurpose existing ones to make advancements towards achieving a more complete sense of grounding.

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Section: Learning Objectivementioning

confidence: 99%

Section: New Datasetsmentioning

confidence: 99%

Grounding ‘Grounding’ in NLP

Chandu

Bisk

Black

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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“…The referring expressions were extracted from the tutorial dialogues and their semantic segments and labels were manually annotated. A linear-chain CRF was trained on that data and used to perform referring expression segmentation and labeling (Li and Boyer, 2015). The current paper reports the first use of that learned semantics approach for reference resolution.…”

Section: Semantic Parsingmentioning

confidence: 99%

Reference Resolution in Situated Dialogue with Learned Semantics

Boyer

2016

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Understanding situated dialogue requires identifying referents in the environment to which the dialogue participants refer. This reference resolution problem, often in a complex environment with high ambiguity, is very challenging. We propose an approach that addresses those challenges by combining learned semantic structure of referring expressions with dialogue history into a ranking-based model. We evaluate the new technique on a corpus of human-human tutorial dialogues for computer programming. The experimental results show a substantial performance improvement over two recent state-of-the-art approaches. The proposed work makes a stride toward automated dialogue in complex problem-solving environments.

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“…A linear-chain CRF was trained on that data and used to perform referring expression segmentation and labeling (Li and Boyer, 2015). The current paper reports the first use of that learned semantics approach for reference resolution.…”

Section: Semantic Parsingmentioning

confidence: 99%

“…First, referring expressions from the situated dialogue are segmented and labeled according to their semantic structure. Using a semantic segmentation and labeling approach we have previously developed (Li and Boyer, 2015), we use a conditional random field (CRF) for this joint segmentation and labeling task, and the values of the labeled attributes are then extracted (Section 3.1). The result of this step is learned semantics, which are attributes of objects expressed within each referring expression.…”

Section: Reference Resolution Approachmentioning

confidence: 99%

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Katagiri

Nakano

Fernández

et al. 2016

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We extend special thanks to our Local co-Chairs, Ron Artstein and Alesia Gainer, and their team of student volunteers. We know SIGDIAL 2016 would not have been possible without Ron and Alesia, who invested so much effort in arranging the conference venue and accommodations, handling registration, making banquet arrangements, and handling numerous other preparations for the conference. The student volunteers for on-site assistance also deserve our appreciation.Ethan Selfridge, Sponsorships Chair, has earned our appreciation for recruiting and liaising with our conference sponsors, many of whom continue to contribute year after year. Sponsorships support valuable aspects of the program, such as the invited speakers and conference banquet. In recognition of this, we gratefully acknowledge the support of our sponsors: (Platinum level) Microsoft Research, Xerox and PARC, Intel, (Gold level) Facebook, (Silver level) Amazon Alexa, Interactions, Educational Testing Service, Honda Research Institute, and Yahoo!. At the same time, we thank Priscilla Rasmussen at the ACL for tirelessly handling the financial aspects of sponsorship for SIGDIAL 2016, and for securing our ISBN.iii We also thank the SIGdial board, especially officers Amanda Stent, Jason Williams and Kristiina Jokinen for their advice and support from beginning to end.Finally, we thank all the authors of the papers in this volume, and all the conference participants for making this stimulating event a valuable opportunity for growth in the research areas of discourse and dialogue. AbstractThis paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent QNetworks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state. IntroductionTask-oriented dialog systems have been an important branch of spoken dialog system (SDS) research (Raux et al., 2005; Young, 2006; Bohus and Rudnicky, 2003). The SDS agent has to achieve some predefined targets (e.g. booking a flight) through natural language interaction with the users. The typical structure of a task-oriented dialog system is outlined in Figure 1 (Young, 2006). This pipeline consists of several independently-developed modules: natural language understanding (the NLU) maps the user utterances to some semantic representation. This information is further processed by the dialog state tracker (DST), which accumulates the input of the turn along with the dialog history. The DST outputs the current dialog state and the dialog policy selects the next system action based on the dialog state. Then natural language gene...

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Semantic Grounding in Dialogue for Complex Problem Solving

Cited by 4 publications

References 25 publications

Grounding ‘Grounding’ in NLP

Grounding ‘Grounding’ in NLP

Reference Resolution in Situated Dialogue with Learned Semantics

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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