Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work on question generation assumes knowledge of what the answer might be . instead, questioner must reason pragmatically about how to acquire new information . |
| Approach: | They propose a question generation system that generates pragmatically relevant questions in information-asymmetric conversations. |
| Outcome: | The proposed questioner significantly improves the informativeness and specificity of questions generated over a baseline model as evaluated by metrics as well as humans. |
Similar Papers
Exploring Question-Specific Rewards for Generating Deep Questions (2020.coling-main)
Copied to clipboard
| Challenge: | Recent question generation approaches use the sequence-to-sequence framework to optimize the log likelihood of ground-truth questions using teacher forcing. |
| Approach: | They propose to optimize for QG-specific objectives via reinforcement learning to improve question quality. |
| Outcome: | The proposed model improves the fluency, relevance, and answerability of generated questions. |
Learning to Ask Informative Questions: Enhancing LLMs with Preference Optimization and Expected Information Gain (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) often perform poorly in generating informative questions, as measured by expected information gain (EIG). |
| Approach: | They propose to use a large language model to enhance the informativeness of LLM-generated questions in 20-question game dialogues by applying a Direct Preference Optimization algorithm to generate low-EIG and high-EI questions. |
| Outcome: | The proposed method produces more effective questions even in domains different from those used to train the DPO model. |
Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation (D19-1)
Copied to clipboard
| Challenge: | Existing methods for question generation suffer from dullness and deviation problem, which can lead to deviated or dull questions. |
| Approach: | They propose two methods to enhance semantic coherence between question and answer by using a coherent score and adversarial training to explicitly control question generation. |
| Outcome: | The proposed methods outperform state-of-the-art baseline algorithms with large margins in raising semantic coherent questions. |
Inquisitive Question Generation for High Level Text Comprehension (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing data-driven questions generate questions that fill gaps in knowledge . a dataset of 19K questions is used to generate meaningful questions . |
| Approach: | They propose a dataset of 19K questions that are elicited while a person is reading a document. |
| Outcome: | The proposed model generates reasonable questions, but the task is challenging. |
PragmatiCQA: A Dataset for Pragmatic Question Answering in Conversations (2023.findings-acl)
Copied to clipboard
| Challenge: | Mars? - PragmatiCQA |
| Approach: | Mars? - The Paper . |
| Outcome: | The proposed dataset features 6873 QA pairs that explores pragmatic reasoning in conversations over a diverse set of topics. |
Ask to Learn: A Study on Curiosity-driven Question Generation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work on Question Generation focuses on generating relevant questions given text with an answer . human ability to ask questions goes beyond evaluation of reading comprehension . |
| Approach: | They propose a novel text generation task based on a conversational question-asking dataset . they investigate automated metrics to measure different properties of Curious Questions . |
| Outcome: | The proposed task is based on a conversational Question Answering dataset . the results show that humans tend to ask questions with the goal of obtaining new information . |
Asking More Informative Questions for Grounded Retrieval (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to question generation for interactive retrieval have constrained answer spaces, limiting the amount of information a model can gain in a single turn. |
| Approach: | They propose a method that incorporates presupposition handling into question selection and belief updates. |
| Outcome: | The proposed method increases accuracy over the past state-of-the-art by 14% while resulting in 48% more efficient games in human evaluations. |
TRAVEL: Tag-Aware Conversational FAQ Retrieval via Reinforcement Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods aim to fully utilize the dynamic conversation context to enhance the semantic association between the user query and FAQ questions, but they are limited by noise and e.g., users may click questions they don't like, leading to inaccurate semantics modeling. |
| Approach: | They propose to introduce tags of FAQ questions to reduce noise in the conversation context and integrate them into a reinforcement learning framework to minimize the negative impact of irrelevant information. |
| Outcome: | The proposed method can eliminate irrelevant information and minimize negative impact of irrelevant information in the dynamic conversation context. |
Generating Deep Questions with Commonsense Reasoning Ability from the Text by Disentangled Adversarial Inference (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for commonsense question generation produce shallow questions that can be answered by simple word matching. |
| Approach: | They propose a task of commonsense question generation that aims to yield deep-level questions from the text. |
| Outcome: | The proposed model can yield deep-level and to-the-point questions from the text. |
Asking It All: Generating Contextualized Questions for any Semantic Role (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to question generation require conditioning on existing answers in text . previous work required human-curated templates, limiting coverage and question fluency . |
| Approach: | They propose a task of role question generation that produces a prototype and revises it to be contextually appropriate for the passage. |
| Outcome: | The proposed model generates diverse and well-formed questions for a large, broad-coverage ontology of predicates and roles. |