Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading
Not on the map. Why?
Abstract (arXiv)
Conversational Machine Reading (CMR) requires answering a user's initial question through multi-turn dialogue interactions based on a given document. Although there exist many effective methods, they largely neglected the alignment between the document and the user-provided information, which significantly affects the intermediate decision-making and subsequent follow-up question generation. To address this issue, we propose a pipeline framework that (1) aligns the aforementioned two sides in an explicit way, (2)makes decisions using a lightweight many-to-many entailment reasoning module, and (3) directly generates follow-up questions based on the document and previously asked questions. Our proposed method achieves state-of-the-art in micro-accuracy and ranks the first place on the public leaderboard of the CMR benchmark dataset ShARC.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 31 of its 45 references that are in the map. First posted on arXiv 20 Oct 2023.
Builds on
31 of 45 references.
References · 31
- 2017Attention Is All You Needcited by 59,932
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
- 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
- 2015A large annotated corpus for learning natural language inferencecited by 1,837
- 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
- 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
19 more
- 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,219
- 2015Teaching Machines to Read and Comprehendcited by 1,039
- 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 851
- 2016NewsQA: A Machine Comprehension Datasetcited by 330
- 2021Entailment as Few-Shot Learnercited by 122
- 2019Semantics-aware BERT for Language Understandingcited by 111
- 2018Interpretation of Natural Language Rules in Conversational Machine Readingcited by 72
- 2018DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehensioncited by 70
- 2021MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documentscited by 53
- 2016Tracking the World State with Recurrent Entity Networkscited by 51
- 2017Quasar: Datasets for Question Answering by Search and Readingcited by 50
- 2015Natural Language Inference by Tree-Based Convolution and Heuristic Matchingcited by 42
- 2020Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Readingcited by 33
- 2017Large-scale Cloze Test Dataset Created by Teacherscited by 27
- 2020Dialogue Graph Modeling for Conversational Machine Readingcited by 26
- 2016Who did What: A Large-Scale Person-Centered Cloze Datasetcited by 24
- 2019E3: Entailment-driven Extracting and Editing for Conversational Machine Readingcited by 12
- 2019Attending to Future Tokens For Bidirectional Sequence Generationcited by 12
- 2020Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Readingcited by 11
Built on by
Citing papers per year: 2026: 1.
1 paper.