| Challenge: | a neural model for characterizing individual differences in speakers is shown to be useful in human-computer interaction and dialog act prediction. |
| Approach: | They propose a neural model for learning a dynamically updated speaker embedding in a conversational context. |
| Outcome: | The proposed model is used for content ranking and dialog act prediction in human-human conversations. |
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Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models (L18-1)
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| Challenge: | Existing methods for speaker modeling are based on hand-crafted statistics and ad hoc to a certain application. |
| Approach: | They propose to use speaker classification as a surrogate task for general speaker modeling and collect massive data to facilitate research in this direction. |
| Outcome: | The proposed models outperform the existing models and are feasible with speaker identity information. |
Predicting Reference: What do Language Models Learn about Discourse Models? (2020.emnlp-main)
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| Challenge: | a growing literature that probes neural language models to assess their latent acquisition of grammatical knowledge has not investigated their acquisition of discourse modeling ability. |
| Approach: | They draw on a psycholinguistic literature that has established how different contexts affect referential biases concerning who is likely to be referred to next. |
| Outcome: | The proposed models do not resemble human language users, the authors show . their models capture the linguistic knowledge required to perform discourse modeling . |
Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk (2022.acl-srw)
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| Challenge: | a new study shows that dialog systems that can hold social talk and make sense of conversational content are not efficient for context-sensitive natural language understanding and reasoning. |
| Approach: | They propose a linguistically-informed architecture to handle social talk in English . they propose linguistic models that fit the context-sensitive components into a Bayesian game-theoretic model . |
| Outcome: | The proposed architecture is based on corpus-based methods but does not track what is happening in a conversation. |
Neural Conversation Recommendation with Online Interaction Modeling (D19-1)
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| Challenge: | Existing models that only use lexical features and ignore past user interactions in online conversations are inadequate to identify and engage in online discussions. |
| Approach: | They propose a framework that automatically recommends conversations based on user's prior conversation behaviors by exploring deep semantic features that measure how a user’s preferences match an ongoing conversation’s context. |
| Outcome: | The proposed model outperforms state-of-the-art models on two large-scale datasets from Twitter and Reddit showing that it incorporates deep semantic features that measure how a user’s preferences match an ongoing conversation’s context. |
Dynamic Online Conversation Recommendation (2020.acl-main)
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| Challenge: | Existing models that assume static user interests are unable to capture the temporal aspects of user interactions and interest changes over time. |
| Approach: | They propose a neural architecture to exploit changes of user interactions and interests over time to predict which discussions they are likely to enter. |
| Outcome: | The proposed model outperforms state-of-the-art models that assume static user interests and handle future conversations that are unseen during training time. |
Session-level Language Modeling for Conversational Speech (D18-1)
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| Challenge: | Xiong et al., 2017) generalizes language models for conversational speech recognition . recurrent neural networks (RNNs) read a list of words sequentially and predict the next word at each position. |
| Approach: | They propose to generalize language models for conversational speech recognition to capture conversation-level phenomena such as adjacency pairs, lexical entrainment, and topical coherence. |
| Outcome: | The proposed model reduces perplexity and improves word error rate over standard models in the conversational telephone speech domain. |
Who is Speaking? Speaker-Aware Multiparty Dialogue Act Classification (2023.findings-emnlp)
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| Challenge: | Identifying how speakers interact with each other in a conversation is difficult when more than two interlocutors take part in . To overcome this challenge, we propose to explicitly add speaker awareness to each utterance representation. |
| Approach: | They propose to add speaker awareness to each utterance representation to model how each speaker is behaving within the local context of a conversation. |
| Outcome: | The proposed approach is able to model multiparticipant and dyadic conversations on the MRDA and SwDA datasets and shows that it is more efficient than previous approaches. |
Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues (N19-1)
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| Challenge: | Recent surge of text-based online counseling applications enables us to collect and analyze interactions between counselors and clients. |
| Approach: | They develop a pre-trained conversation model that learns to classify client utterances into categories that help counselors in diagnosing client status and predicting counseling outcome. |
| Outcome: | The proposed model outperforms state-of-the-art comparison models and shows expected linguistic patterns for each category. |
Learning Personas from Dialogue with Attentive Memory Networks (D18-1)
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| Challenge: | Existing systems that can infer persona from dialogue can be used for computational narrative analysis and personalized dialogue generation. |
| Approach: | They propose neural models to learn persona embeddings in a character trope classification task using IMDB dialogue snippets. |
| Outcome: | The proposed methods could be applied to other domains, including personalized dialogue generation. |
Improving Spoken Language Modeling with Phoneme Classification: A Simple Fine-tuning Approach (2024.emnlp-main)
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| Challenge: | Generating speech through a pipeline that operates at the text level typically loses nuances, intonations, and non-verbal vocalizations. |
| Approach: | They show that fine-tuning speech representation models on phoneme classification leads to more context-invariant representations, and language models trained on these units achieve comparable lexical comprehension to ones trained on hundred times more data. |
| Outcome: | Recent advances in speech representation modeling have shown that learning language directly from speech is feasible. |