| Challenge: | Recent advances in deep learning and semantic parsing have improved the translation accuracy of natural language questions to structured queries. |
| Approach: | They propose a dialogue-based structured query generation framework that leverages human intelligence to boost performance of existing algorithms via user interaction. |
| Outcome: | The proposed framework improves on a WikiSQL dataset from 61.3% to 69.0% using only 2.4 validation questions per dialogue. |
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SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (2022.aacl-main)
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| Challenge: | a paper focuses on the generation of natural language questions based on SPARQL queries . knowledge-based approaches have become popular in the field of question answering and dialogue . |
| Approach: | This paper focuses on the generation of natural language questions based on SPARQL queries . it uses 4 knowledge-based QA corpora homogenized for the task and a new challenge set is introduced . |
| Outcome: | The proposed task is based on the generation of questions in a conversational context. |
Adversarial Learning on the Latent Space for Diverse Dialog Generation (2020.coling-main)
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| Challenge: | Existing methods for dialog generation generate generic utterances, e.g., always generating "I don't know" |
| Approach: | They propose a framework that uses generative adversarial nets to generate conditioned responses in dialogs. |
| Outcome: | The proposed model generates more fluent, relevant, and diverse responses than state-of-the-art methods. |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)
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Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev
| Challenge: | CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems. |
| Approach: | They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries . |
| Outcome: | The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains. |
Semantic Parsing with Syntax- and Table-Aware SQL Generation (P18-1)
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Yibo Sun, Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou
| Challenge: | Existing approaches generate a SQL query word-by-word but results are incorrect or not executable due to mismatch between question words and table contents. |
| Approach: | They propose a generative model to map natural language questions into SQL queries. |
| Outcome: | The proposed model significantly improves state-of-the-art execution accuracy from 69.0% to 74.4% on a large question- SQL dataset. |
Dialog Generation Using Multi-Turn Reasoning Neural Networks (N18-1)
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| Challenge: | Existing methods for dialog generation are limited and short at generalization. |
| Approach: | They propose a generalizable dialog generation approach that adapts multi-turn reasoning to generate responses by taking current conversation session context as a document and current query as 'question' they separate the single memory used for document comprehension into different groups for speaker-specific topic and opinion embedding. |
| Outcome: | Experiments on Japanese 10-sentence (5-round) conversation modeling show that multi-turn reasoning can produce more diverse and acceptable responses than state-of-the-art single-turn and non-reasoning baselines. |
q2d: Turning Questions into Dialogs to Teach Models How to Search (2023.emnlp-main)
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| Challenge: | Recent dialog generation models use external search APIs to generate grounded responses. |
| Approach: | They propose an automatic data generation pipeline that generates dialogs from questions . they use a large language model to create conversational versions of question answering datasets . |
| Outcome: | The proposed method improves query generation models on a QReCC dataset. |
ConvGQR: Generative Query Reformulation for Conversational Search (2023.acl-long)
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| Challenge: | Existing methods to determine a good search query from the whole conversation context are expensive and often lead to sub-optimal results. |
| Approach: | They propose a framework to reformulate conversational queries based on generative pre-trained language models (PLMs) they propose generative knowledge infusion mechanism to optimize query reformulation and retrieval. |
| Outcome: | Extensive experiments on four conversational search datasets demonstrate the effectiveness of ConvGQR. |
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory (N19-1)
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| Challenge: | Existing generative dialogue models generate responses from input queries . however, the results are limited and the models are unsatisfactory . |
| Approach: | They propose a framework which exploits retrieval results via a skeleton-to-response paradigm . they extract a query skelet and use it to generate a new skele and response . |
| Outcome: | The proposed approach significantly improves the informativeness of the generated responses. |
Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources (2023.emnlp-main)
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| Challenge: | Existing dialog inpainting methods generate ConvQA datasets with low contextual relevance due to insufficient learning of question-answer alignment. |
| Approach: | They propose a dialog inpainting method that generates ConvQA datasets from documents . they propose re-ranking tasks and a framework that generate contextually relevant questions . |
| Outcome: | The proposed framework generates ConvQA datasets with high contextual relevance from textual sources. |
Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning (2020.findings-emnlp)
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| Challenge: | Sentence function is an important linguistic feature indicating the communicative purpose of a sentence in a conversation. |
| Approach: | They propose a structured meta-learning approach for dialogue generation on infrequent sentence functions. |
| Outcome: | The proposed approach improves informativeness and relevance of dialogue generation on infrequent sentence functions while preserving knowledge generalization for similar sentence functions. |