Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading (2020.emnlp-main)
Copied to clipboard
Yifan Gao, Chien-Sheng Wu, Jingjing Li, Shafiq Joty, Steven C.H. Hoi, Caiming Xiong, Irwin King, Michael Lyu
| Challenge: | Document interpretation and dialog understanding are the two major challenges for conversational machine reading. |
| Approach: | They propose a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding of document and dialog. |
| Outcome: | The proposed model improves document interpretation and dialog understanding on the ShARC benchmark. |
Similar Papers
Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent research has explored how to improve the abilities of decision-making and question generation. |
| Approach: | They propose a pipeline framework that aligns the document and user-provided information in an explicit way, makes decisions using a lightweight many-to-many entailment reasoning module and generates follow-up questions based on the document. |
| Outcome: | The proposed framework achieves state-of-the-art in micro-accuracy and ranks the first place on the public leaderboard of the CMR benchmark dataset ShARC. |
Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading (2020.acl-main)
Copied to clipboard
Yifan Gao, Chien-Sheng Wu, Shafiq Joty, Caiming Xiong, Richard Socher, Irwin King, Michael Lyu, Steven C.H. Hoi
| Challenge: | Existing approaches to answer user questions are limited in their decision making due to struggles in extracting question-related rules and reasoning about them. |
| Approach: | They propose a conversational machine reading framework that uses a Explicit Memory Tracker to track whether conditions in the rule text have already been satisfied to make a decision. |
| Outcome: | The proposed framework achieves state-of-the-art on the ShARC benchmark and is more interpretable by visualizing the entailment-oriented reasoning process as the conversation flows. |
E3: Entailment-driven Extracting and Editing for Conversational Machine Reading (P19-1)
Copied to clipboard
| Challenge: | Conversational machine reading systems help users answer high-level questions when they do not know the exact rules by which the decision is made. |
| Approach: | They propose a conversational machine reading model that extracts a set of decision rules from a procedural text which the system must read to figure out what to ask the user. |
| Outcome: | The proposed model outperforms existing systems and a BERT-based baseline on the ShARC conversational machine reading dataset and provides an explainable alternative to prior work. |
DAGN: Discourse-Aware Graph Network for Logical Reasoning (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent QA with logical reasoning questions requires passage-level relations among the sentences. |
| Approach: | They propose a discourse-aware graph network that aggregates passage-level clues for QA by using discourse-based information. |
| Outcome: | The proposed model achieves competitive results on two logical reasoning QA datasets. |
DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies on ERC lack the ability to extract and integrate emotional clues from the conversational context. |
| Approach: | They propose a new model that uses multi-turn reasoning modules to extract and integrate emotional clues from conversational context. |
| Outcome: | The proposed model outperforms existing models on three public benchmark datasets and is highly effective and superior to existing models. |
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension (2023.acl-long)
Copied to clipboard
| Challenge: | Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversation scenes. |
| Approach: | They propose a one-stage end-to-end framework to bridge the information gap between decision-making and question generation in a global understanding manner. |
| Outcome: | The proposed framework achieves new state-of-the-art performance on the OR-ShARC benchmark. |
ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods require three steps to understand text, but span extraction and question rephrasing steps are not fully exploited. |
| Approach: | They propose a framework for conversational machine reading comprehension based on shared parameter mechanism . experimental results show the proposed framework achieves new state-of-the-art results on the ShARC leaderboard . |
| Outcome: | The proposed framework achieves state-of-the-art on the ShARC leaderboard with the BLEU-4 score of 55.2. |
Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk (2022.acl-srw)
Copied to clipboard
| 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. |
Towards End-to-End Open Conversational Machine Reading (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing approaches to the problem of open-retrieval conversational machine reading (OR-CMR) use two separate modules to approach the problem's two successive sub-tasks. |
| Approach: | They propose to model OR-CMR as a unified text-to-text task in a fully end-to end style and propose to use a text-based approach to solve the problem. |
| Outcome: | Experiments on the ShARC and OR-ShARC dataset show that the proposed framework can generalize to different backbone models. |
FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension (D19-58)
Copied to clipboard
| Challenge: | Existing machine comprehension models focus on a single-turn setting and do not account for previous reasoning processes. |
| Approach: | They propose to explicitly model the information gain through the dialogue reasoning . they propose to apply the proposed mechanism to other machine comprehension models . |
| Outcome: | The proposed model achieves state-of-the-art performance in a conversational QA dataset QuAC and a sequential instruction understanding dataset SCONE. |