Papers with CoQA
CoQAR: Question Rewriting on CoQA (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing systems that ask questions in a conversational context may have contextual dependencies that make the understanding difficult. |
| Approach: | They propose to rewrite questions into an out-of-context form to facilitate understanding . they propose to use this form to train and evaluate conversational question answering models . |
| Outcome: | The proposed model can be used in the supervised learning of three tasks: question paraphrasing, question rewriting and conversational question answering. |
Fluent Response Generation for Conversational Question Answering (2020.acl-main)
Copied to clipboard
| Challenge: | Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA subtask. |
| Approach: | They propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness. |
| Outcome: | The proposed model outperforms baseline CoQA and QuAC models in generating conversational responses. |
CoHS-CQG: Context and History Selection for Conversational Question Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing studies focus on single-turn question generation, but few studies have studied the challenges of multiturn QG. |
| Approach: | They propose a two-stage conversational question generation framework that shortens the context and history of the input and calculates relevance scores. |
| Outcome: | The proposed framework achieves state-of-the-art on CoQA in answer-aware and answer-unaware settings. |
Interview Evaluation: A Novel Approach for Automatic Evaluation of Conversational Question Answering Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing evaluation methods for CQA use pre-collected human-human conversations . previous methods use model-predicted dialogue history instead of ground truth . |
| Approach: | They propose an automatic evaluation approach that uses the model's dialogue history to evaluate models. |
| Outcome: | The proposed method improves on existing models and their evaluations on QuAC and CoQA. |
A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC (N19-1)
Copied to clipboard
| Challenge: | In response to this development, there have been a flurry of new datasets for question answering. |
| Approach: | They propose to use SQuAD 2.0, QuAC, and CoQA to provide question answering on textual data. |
| Outcome: | The proposed datasets provide complementary coverage of the first two aspects, but weak coverage of third. |
Conversational Machine Comprehension: a Literature Review (2020.coling-main)
Copied to clipboard
| Challenge: | Conversational machine comprehension (CMC) is a research track in conversational AI. |
| Approach: | They propose to synthesize a generic framework for CMC models and highlight differences in recent approaches. |
| Outcome: | The proposed model will be used as a compendium for future research. |
Do not let the history haunt you: Mitigating Compounding Errors in Conversational Question Answering (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing approaches employ human-written ground-truth answers for answering conversational questions at test time, but in a realistic scenario, the CoQA model will not have access to ground-Truth answers. |
| Approach: | They propose a sampling strategy that dynamically selects between target answers and model predictions during training, closely simulating the situation at test time. |
| Outcome: | The proposed sampling strategy closely simulates the situation at test time and significantly lowers the performance of CoQA systems. |
Synthesize, Prompt and Transfer: Zero-shot Conversational Question Generation with Pre-trained Language Model (2023.acl-long)
Copied to clipboard
| Challenge: | Existing research on QG focuses on generating single-turn questions, which are formalized as independent interactions. |
| Approach: | They propose a multi-stage knowledge transfer framework to leverage knowledge from single-turn question generation instances. |
| Outcome: | The proposed framework achieves 14.81 BLEU-4 (88.2% absolute improvement compared to T5) in CoQA with knowledge transferred from three single-turn datasets. |
Generating Extractive Answers: Gated Recurrent Memory Reader for Conversational Question Answering (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Conversational question answering (CQA) requires models to extract answers from given contents to answer follow-up questions according to conversation history. |
| Approach: | They propose a novel architecture that integrates extractive MRC models into a generalized sequence-to-sequence framework. |
| Outcome: | The proposed architecture can use less storage space and consider historical memory deeply and selectively. |
Uncertainty Quantification of Large Language Models through Multiple Uncertainty Sources (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for uncertainty quantification fail to capture multifaceted nature of natural language generation. |
| Approach: | They propose a multi-resource Uncertainty Quantification framework that integrates heterogeneous uncertainty signals into a unified measure. |
| Outcome: | The proposed framework outperforms existing methods on CoQA, NQ_Open, and HotpotQA. |
Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing knowledge transfer models do not exploit compositionality of language, often relying on superficial features. |
| Approach: | They propose to use a knowledge distillation technique to fine tune RoBERTa, BERT and DistilBERT models to improve their performance. |
| Outcome: | The proposed models improve on the CoQA task with linguistic knowledge and are able to represent compositional and lexical information. |
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to machine reading comprehension (MRC) on long texts typically chunk text into equally-spaced segments without considering information from other segments. |
| Approach: | They propose to let a model learn to chunk in a more flexible way via reinforcement learning. |
| Outcome: | The proposed model extracts a text span from document and query as answer . previous models can only take a fixed-length (e.g., 512) text as input . |
MCˆ2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension (P19-1)
Copied to clipboard
| Challenge: | Existing models combine previous questions for conversation understanding and only employ recurrent neural networks (RNN) for reasoning. |
| Approach: | They propose a multi-perspective convolutional cube model that integrates 1D and 2D convolutions with recurrent neural networks (RNN) to understand context from different perspectives. |
| Outcome: | The proposed model is based on the Conversational Question Answering (CoQA) dataset and achieves state-of-the-art results. |