Interview: Large-scale Modeling of Media Dialog with Discourse Patterns and Knowledge Grounding (2020.emnlp-main)
Copied to clipboard
| Challenge: | Discourse analysis has been limited to small news corpora, but this study is expanding to tens of thousands of interviews. |
| Approach: | They propose a large-scale analysis of discourse in media dialog and its impact on dialog modeling with a focus on interrogative patterns and use of external knowledge. |
| Outcome: | The proposed model outperforms strong discourse-agnostic baselines for dialog modeling, generating more specific and topical responses in interview-style conversations. |
Similar Papers
MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing datasets for dialogue summarization are limited to their small sizes and are built from a narrow domain. |
| Approach: | They propose a large-scale media interview dataset consisting of 463.6K transcripts with abstractive summaries. |
| Outcome: | The proposed dataset is larger and contains multi-party conversations from multiple domains. |
Construction and Analysis of a Multimodal Chat-talk Corpus for Dialog Systems Considering Interpersonal Closeness (2020.lrec-1)
Copied to clipboard
| Challenge: | a large-scale multimodal dialog corpus is needed to accelerate research on dialog systems that can handle social signals and verbal information. |
| Approach: | They construct a multimodal dialog corpus focusing on the relationship between speakers and 19 pairs of participants. |
| Outcome: | The proposed system is based on a multimodal dialog corpus of 19,303 utterances (10 hours) from 19 pairs of participants. |
SPORTSINTERVIEW: A Large-Scale Sports Interview Benchmark for Entity-centric Dialogues (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing knowledge grounded dialogue datasets only contain external knowledge from one dimension, which limits the diversity of knowledge sources and may contain unwanted bias. |
| Approach: | They propose to use two types of external knowledge sources as knowledge grounding in an interview dataset to model human dialogues. |
| Outcome: | The proposed dataset contains 150K interviews and 34K interviewees . it is larger in size and has more than one dimension of external knowledge linking . however, the performance of the proposed models is far from humans . |
Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study (2024.lrec-main)
Copied to clipboard
| Challenge: | Large language models have shown remarkable capability in many downstream tasks, yet their ability to understand discourse structures of dialogues remains less explored. |
| Approach: | They aim to systematically inspect ChatGPT’s performance in two discourse analysis tasks: topic segmentation and discourse parsing. |
| Outcome: | The proposed model can give more reasonable topic structures than human annotations but only linearly parses the hierarchical rhetorical structures. |
DialogSum: A Real-Life Scenario Dialogue Summarization Dataset (2021.findings-acl)
Copied to clipboard
| Challenge: | Experimental results show unique challenges in dialogue summarization such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense. |
| Approach: | They propose a large-scale labeled dialogue summarization dataset . they use state-of-the-art neural models to analyze spoken dialogue summaries . |
| Outcome: | The proposed dataset can be used to analyze spoken dialogue summarization challenges. |
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation (2020.acl-demos)
Copied to clipboard
Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
| Challenge: | DIALOGPT is a large, tunable neural conversational response generation model . trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Approach: | They present a large, tunable neural conversational response generation model, DIALOGPT . the model is trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Outcome: | The proposed model can generate more relevant, contentful and context-consistent responses than baseline systems. |
Towards Exploiting Background Knowledge for Building Conversation Systems (D18-1)
Copied to clipboard
| Challenge: | Existing dialog datasets contain a sequence of utterances without any explicit background knowledge associated with them. |
| Approach: | They propose to use movie chats to generate responses by copying unstructured background knowledge . they use a dataset of 9K conversations to test whether responses are generated by copy-and-modify models . |
| Outcome: | The proposed model mimics human process of conversing by copying and/or modifying sentences from unstructured background knowledge. |
SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Empirical studies show that supervised learning is extremely effective in in-domain datasets and models trained on SuperDialseg can achieve good generalization ability on out-of-domain data. |
| Approach: | They propose a supervised definition of dialogue segmentation points using document-grounded dialogues and a large-scale supervised dataset called SuperDialseg. |
| Outcome: | The proposed model can achieve good generalization ability on out-of-domain data. |
SAD: A Large-Scale Strategic Argumentative Dialogue Dataset (2026.acl-long)
Copied to clipboard
YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song, Hinrich Schuetze
| Challenge: | Argumentation is a key part of human reasoning and decision-making . existing argumentative corpora focus on single-turn settings, but multi-turn dialogues are often realized as multi-turned dialogues . |
| Approach: | They present a dataset for strategic multi-turn argumentation dialogues . they annotate each utterance with five strategy types, allowing multiple strategies per utterrance . |
| Outcome: | The proposed dataset shows that explicit prompting improves fluency, stylistic coherence and persuasiveness. |
Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. |
| Approach: | They propose a posterior-based reweighing and noisy training strategy to exploit generated knowledge in dialogue generation. |
| Outcome: | Empirical results show that the proposed methods outperform the state-of-the-art methods in unsupervised knowledge-grounded conversation. |