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. |
Similar Papers
Reinforced Dynamic Reasoning for Conversational Question Generation (P19-1)
Copied to clipboard
| Challenge: | Empirical results on the recently released CoQA dataset demonstrate the effectiveness of our method . large-scale highquality conversational question answering datasets such as CoQA and QuAC can help train models to answer sequential questions. |
| Approach: | They propose a task called Conversational Question Generation which generates a question based on a passage and a conversation history to generate the next question. |
| Outcome: | The proposed method is based on a question-answering style conversation dataset . it can be used to generate meaningful questions on QA and SQuAD datasets . |
ChatR1: Reinforcement Learning for Conversational Reasoning and Retrieval Augmented Question Answering (2026.acl-long)
Copied to clipboard
| Challenge: | Unlike static ‘rewrite, retrieve, and generate’ pipelines, ChatR1 interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through RL. |
| Approach: | They propose a reasoning framework based on reinforcement learning (RL) for conversational question answering that interleaves search and reasoning across turns and provides turn-level feedback. |
| Outcome: | The proposed framework outperforms competing models on five CQA datasets, measured by different metrics (F1, BERTScore, and LLM-as-judge). |
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 . |
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. |
RAC: Retrieval-augmented Conversation Dataset for Open-domain Question Answering in Conversational Settings (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Existing studies constrain questions and answers within predefined contexts, excluding the retrieval process. |
| Approach: | They present a retrieval-augmented conversation dataset that addresses key challenges . they propose a system that combines query rewriting and retrieval with reranking . |
| Outcome: | The proposed system improves query rewriting, retrieval, reranking, and response generation performance. |
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension (D19-58)
Copied to clipboard
| Challenge: | Existing question reformulation models are based on supervised question labels without considering feedback information from answers. |
| Approach: | They propose a question reformulation model that integrates conversational history information with reinforcement learning. |
| Outcome: | The proposed model is more effective in conversational machine comprehension with reinforcement learning. |
Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data (P19-1)
Copied to clipboard
| Challenge: | Existing QA methods lack scalability and performance is difficult to solve with document-level contexts. |
| Approach: | They propose an end-to-end deep network model that sequentially reads the input contexts into an external memory while replacing memories that are less important for answering unseen questions. |
| Outcome: | The proposed model improves on a synthetic dataset and real-world large-scale textual and video QA datasets. |
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. |
Conversational QA Dataset Generation with Answer Revision (2022.coling-1)
Copied to clipboard
| Challenge: | Existing frameworks for conversational question-answer generation generate a large-scale dataset based on input passages. |
| Approach: | They propose a conversational question-answer generation framework that extracts question-worthy phrases from passages and generates corresponding questions considering previous conversations. |
| Outcome: | The proposed framework improves the quality of synthetic data and can be used for domain adaptation of conversational question answering. |
Integrating Question Rewrites in Conversational Question Answering: A Reinforcement Learning Approach (2022.acl-srw)
Copied to clipboard
| Challenge: | Existing approaches to improve QR performance dependencies among dialogue history dependencies are limited. |
| Approach: | They propose a reinforcement learning approach that integrates QR and CQA tasks without corresponding labeled QR datasets. |
| Outcome: | The proposed approach improves existing pipeline approaches in conversational question answering (QA) existing methods depend on assumption of corresponding QR datasets for every CQA dataset, resulting in poor performance. |