Dynamic Composition for Conversational Domain Exploration

Szpektor, Idan; Cohen, Dan; Elidan, Gal; Fink, Michael; Hassidim, Avinatan; Keller, Orgad; Kulkarni, Sayali; Ofek, E. O.; Pudinsky, Sagie; Revach, Asaf; Salant, Shimi; Matias, Yossi

doi:10.1145/3366423.3380167

Cited by 9 publications

(11 citation statements)

References 42 publications

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“…We first evaluate our methods using offline data. We then describe the deployment of our system "in the wild" in the Google Assistant, specifically as part of the animal domain experience described by Szpektor et al [38]. We demonstrate the effectiveness of our RL-based approach at dynamic planning and driving openended dialogue: relative to a SOTA non-RL (transformer) baseline, our bot substantially improves a number of key metrics, including conversation length, cooperative responses and explicit positive feedback.…”

Section: Introductionmentioning

confidence: 89%

“…In this work, we build on the dynamic composition approach introduced by Szpektor et al [38]. This dialogue management model limits the action space using specific content providers to propose candidate utterances, which are dynamically selected by the dialogue manager (DM).…”

Section: Dynamic Compositionmentioning

confidence: 99%

“…This step is repeated until the DM assesses that the response is a relevant and engaging bot response. In [38], the DM is implemented as an RNN encoder, trained in a supervised fashion (see Sec. 4.2).…”

Section: Dynamic Compositionmentioning

confidence: 99%

“…We first exploit powerful supervised models-specifically, RNNs and transformersto provide a succinct embedding of the conversation state. Second, we use the fact that a relatively small set of "reasonable" candidate actions can be generated at each conversation turn [38]. From an RL perspective, this can be viewed as a stochastic realization of the full action space, so we use an RL approach tailored to such stochastic action sets [3].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning

Cohen¹,

Ryu²,

Chow³

et al. 2022

Preprint

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Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans "in the wild" remains a formidable challenge. In this work we develop a real-time, open-ended dialogue system that uses reinforcement learning (RL) to power a bot's conversational skill at scale. Our work pairs the succinct embedding of the conversation state generated using SOTA (supervised) language models with RL techniques that are particularly suited to a dynamic action space that changes as the conversation progresses. Trained using crowd-sourced data, our novel system is able to substantially exceeds the (strong) baseline supervised model with respect to several metrics of interest in a live experiment with real users of the Google Assistant.

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Section: Introductionmentioning

confidence: 89%

Section: Dynamic Compositionmentioning

confidence: 99%

Section: Dynamic Compositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning

Cohen¹,

Ryu²,

Chow³

et al. 2022

Preprint

Self Cite

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“…The output should preserve the information in the input sentences as well as their semantic relationship. It is a crucial component in many NLP applications, including text summarization, question answering and retrieval-based dialogues (Jing and McKeown, 2000;Barzilay and McKeown, 2005;Marsi and Krahmer, 2005;Lebanoff et al, 2019;Szpektor et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Semantically Driven Sentence Fusion: Modeling and Evaluation

Ben-David

Keller

Malmi

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Sentence fusion is the task of joining related sentences into coherent text. Current training and evaluation schemes for this task are based on single reference ground-truths and do not account for valid fusion variants. We show that this hinders models from robustly capturing the semantic relationship between input sentences. To alleviate this, we present an approach in which ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. We apply this method to a large-scale dataset and use the augmented dataset for both model training and evaluation. To improve the learning of semantic representation using multiple references, we enrich the model with auxiliary discourse classification tasks under a multi-tasking framework. Our experiments highlight the improvements of our approach over state-of-the-art models. 1

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Conversational Pattern Mining Using Motif Detection

Garber¹,

Marivate²

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Dynamic Composition for Conversational Domain Exploration

Cited by 9 publications

References 42 publications

Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning

Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning

Semantically Driven Sentence Fusion: Modeling and Evaluation

Conversational Pattern Mining Using Motif Detection

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