Papers with Conversational Question Answering
On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method (2023.tacl-1)
Copied to clipboard
| Challenge: | Existing models show impressive results on a common CQA benchmark, but are they robust to domain, setting and domain? |
| Approach: | They propose a prompt-based history modeling approach that adds textual prompts directly to the text of a passage. |
| Outcome: | The proposed model is simple, easy to plug into practically any model and highly effective. |
Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurement (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing work uses predicted answers instead of unavailable ground-truth answers as conversation history for inference. |
| Approach: | They propose to filter out inaccurate answers in the conversation history without making any architectural changes to the model. |
| Outcome: | The proposed models outperform baselines on two standard ConvQA datasets. |
Reinforced Question Rewriting for Conversational Question Answering (2022.emnlp-industry)
Copied to clipboard
| Challenge: | Existing approaches to CQA involve training new models from scratch . existing approaches are expensive and often not feasible . |
| Approach: | They propose to use QA feedback to supervise the rewriting model with reinforcement learning. |
| Outcome: | The proposed model can improve QA performance over baselines for extractive and retrieval QA. |
Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for Basque (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing datasets for conversational question answering systems are expensive and limited in resources . a dataset of thousands of dialogues and tens of thousands question answering turns is available for free . |
| Approach: | They aim to test the performance of Conversational Question Answering systems in non-English languages . they use a dataset built on top of Wikipedia sections about popular people and organizations . |
| Outcome: | The results show that the system can handle low-resource conditions comparable to English . the results also show that dialogue history models are not directly transferable to another language . |
Towards a more Robust Evaluation for Conversational Question Answering (2021.acl-short)
Copied to clipboard
| Challenge: | Conversational Question Answering (CQA) is a new form of NLP . it uses conversation history to extract the answer of the current question. |
| Approach: | They propose to use conversation history to evaluate models which can access the ground truth answers of previous turns at each turn of the conversation. |
| Outcome: | The proposed evaluation protocol severely limits the effectiveness of the proposed models in fully autonomous chatbots and leads to unsuspected biases in their behavior. |
Capturing Conversational Interaction for Question Answering via Global History Reasoning (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing studies have studied history-dependent reasoning for question answering . utilizing global conversation history for enhancement is gaining interest . |
| Approach: | They propose to establish long-distance dependency among global utterances in multi-turn conversation. |
| Outcome: | The proposed method improves on QuAC by 1%, yielding the F1 score of 73.7%. |
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. |
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. |
Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey (2025.emnlp-main)
Copied to clipboard
Mehrab Tanjim, Yeonjun In, Xiang Chen, Victor Bursztyn, Ryan A. Rossi, Sungchul Kim, Guang-Jie Ren, Vaishnavi Muppala, Shun Jiang, Yongsung Kim, Chanyoung Park
| Challenge: | Existing literature on ambiguity and disambiguation with Large Language Models (LLMs) ambiguities are a fundamental challenge in human-AI interactions due to complexity and flexibility of human language. |
| Approach: | They propose to define key terms and concepts and categorize various disambiguation approaches enabled by LLMs and provide a comparative analysis of their advantages and disadvantages. |
| Outcome: | The proposed frameworks are compared against different disambiguation approaches and highlight their relevance for future research. |
monoQA: Multi-Task Learning of Reranking and Answer Extraction for Open-Retrieval Conversational Question Answering (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to the Conversational Question Answering task have used multi-task learning to solve the task. |
| Approach: | They propose to use multi-task learning to improve the ORConvQA task by sharing the reranker and reader’s learned structure in a generative model. |
| Outcome: | The proposed model outperforms baseline models on the OR-QuAC and OR-CoQA datasets and significantly outperformed existing strong baseline models. |
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. |
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. |
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. |