| Challenge: | QA systems tend to perform poorly at chitchat, while data-driven chatbots are typically user-friendly but not goal-oriented . |
| Approach: | They propose to use a controller to perform dialogue act classification and feed user input either to a sequence-to-sequence chatbot or to QA systems. |
| Outcome: | The proposed system is a spoken QA application for the Google Home smart speaker. |
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Improving Knowledge Production Efficiency With Question Answering on Conversation (2023.acl-industry)
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| Challenge: | Existing researches on conversation-based QA focus on document-based tasks . current researche focuses on document based tasks, but there is a lack of researche on conversation based qa . |
| Approach: | They propose a multi-span extraction model on conversation-based QA and introduce continual pre-training and multi-task learning schemes to further improve model performance. |
| Outcome: | The proposed model outperforms baseline on two Chinese datasets and will be released for research purposes. |
What do Models Learn from Question Answering Datasets? (2020.emnlp-main)
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| Challenge: | Existing models have outperformed humans on question answering datasets, but they have yet to outperform humans on the task of question answering itself. |
| Approach: | They evaluate BERT-based question answering models on their generalizability to out-of-domain examples, responses to missing or incorrect data, and ability to handle question variations. |
| Outcome: | The proposed models outperform human baselines on the widely-used SQuAD 1.1 and SQu AD 2.0 datasets. |
Towards a more Robust Evaluation for Conversational Question Answering (2021.acl-short)
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| 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. |
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NoiseQA: Challenge Set Evaluation for User-Centric Question Answering (2021.eacl-main)
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| Challenge: | Question-Answering (QA) systems are deployed in the real world . a lack of research attention has been devoted to studying the issues that arise when people use QA systems. |
| Approach: | They show that component components that precede an answering engine can introduce varied and considerable sources of error. |
| Outcome: | The proposed evaluations highlight the need for QA evaluation to expand to consider real-world use. |
Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition (2022.naacl-main)
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| Challenge: | a novel AI-empowered chat bot for learning as conversation can be applied to various domains without in-domain dialogue data. |
| Approach: | They propose a novel task where a user does not read a passage but gains information and knowledge through conversation with a teacher bot. |
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DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning (2024.findings-acl)
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| Challenge: | Question answering (QA) is a fundamental task in the field of Natural Language Processing (NLP). |
| Approach: | They propose a database querying and reasoning dataset for question answering that is designed to accommodate sequential questions and multi-hop queries. |
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QAConv: Question Answering on Informative Conversations (2022.acl-long)
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| Challenge: | Experimental results show that state-of-the-art pretrained QA systems have limited zero-shot performance and tend to predict our questions as unanswerable. |
| Approach: | They propose a question-answering dataset that uses conversations as a knowledge source. |
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Deep Chit-Chat: Deep Learning for ChatBots (D18-3)
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| Challenge: | tutorial focuses on building conversational models with deep learning approaches for chatbots. |
| Approach: | This tutorial focuses on building conversational models with deep learning approaches for chatbots. |
| Outcome: | The tutorial summarizes the fundamental challenges in modeling open domain dialogues . it also covers some new trends of research of chatbots - such as how to "control" conversations with specific information . |
Robust Question Answering Through Sub-part Alignment (2021.naacl-main)
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| Challenge: | Current textual question answering models fail to generalize to out-of-domain settings. |
| Approach: | They propose to decompose question and context into smaller units and align them to find the answer. |
| Outcome: | The proposed model is more robust than the standard BERT QA model on adversarial and out-of-domain datasets. |
QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization (D19-1)
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| Challenge: | Existing models are not good at distinguishing distractor sentences which look related but do not answer the question. |
| Approach: | They propose a method to regularize question answering models by maximizing mutual information among passages, questions, and answers. |
| Outcome: | The proposed model achieves state-of-the-art on the Adversarial-SQuAD dataset. |