Sequence to Sequence Learning for Query Expansion

Zaiem, Salah; Sadat, Fatiha

doi:10.1609/aaai.v33i01.330110075

Cited by 4 publications

(2 citation statements)

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“…Recent Query Reformulation. There are recent or concurrent studies (Nogueira and Cho, 2017;Zaiem and Sadat, 2019;Yu et al, 2020;Vakulenko et al, 2020;Lin et al, 2020) that reformulate queries with generation models for other retrieval tasks. However, these studies are not easily applicable or efficient enough for OpenQA because: (1) They require external resources such as paraphrase data (Zaiem and Sadat, 2019), search sessions (Yu et al, 2020), or conversational contexts (Lin et al, 2020;Vakulenko et al, 2020) to form the reformulated queries, which are not available or showed inferior domain-transfer performance in OpenQA (Zaiem and Sadat, 2019);…”

Section: Related Workmentioning

confidence: 99%

“…There have been some recent studies on query reformulation with text generation for other retrieval tasks, which, for example, rewrite the queries to context-independent (Yu et al, 2020;Lin et al, 2020;Vakulenko et al, 2020) or well-formed ones. However, these methods require either task-specific data (e.g., conversational contexts, ill-formed queries) or external resources such as paraphrase data (Zaiem and Sadat, 2019;) that cannot or do not transfer well to OpenQA. Also, some rely on timeconsuming training process like reinforcement learning (RL) (Nogueira and Cho, 2017; that is not efficient enough for OpenQA (more discussions in Sec.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Generation-Augmented Retrieval for Open-Domain Question Answering

Mao¹,

He²,

Liu³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision. We demonstrate that the generated contexts substantially enrich the semantics of the queries and GAR with sparse representations (BM25) achieves comparable or better performance than state-of-the-art dense retrieval methods such as DPR (Karpukhin et al., 2020). We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. Moreover, as sparse and dense representations are often complementary, GAR can be easily combined with DPR to achieve even better performance. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used. 1 * Work was done during internship at Microsoft Azure AI.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generation-Augmented Retrieval for Open-Domain Question Answering

Mao¹,

He²,

Liu³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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SQE-GAN: A Supervised Query Expansion Scheme via GAN

2021

Lecture Notes in Computer Science

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Query Context Expansion for Open-Domain Question Answering

Zhu,

Zhang,

et al. 2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Humans are accustomed to autonomously associating prior knowledge with the text in a query when answering questions. However, machines lacking cognition and common sense, a query is merely a combination of some words. Although we can enrich the semantic information of the given query through language representation or query expansion (QE), the information contained in the query is still insufficient. In this paper, we propose an effective passage retrieval method named query context expansion-based retrieval (QCER) for open-domain question answering (OpenQA). QCER associates a query with domain information by adding contextual association information based on the pseudo-relevance feedback (PRF). QCER uses a dense reader to select top-n expansion terms for QE. We implement QCER by appending reader predictions, theoretically present in candidate passages, as contextual information to the initial query to form the new query. QCER with sparse representations (BM25) can improve retrieval efficiency and accelerate query convergence so that the reader can find the desired answer using fewer relevant passages, e.g., 10 passages, as soon as possible. Moreover, QCER can be easily combined with dense passage retrieval (DPR) to achieve even better performance, as sparse and dense representations are often complementary. Remarkably, we demonstrate that QCER achieves state-of-the-art performance in three tasks, passage retrieval, passage reading, and passage reranking, on the Natural Questions (NQ) and TriviaQA (Trivia) datasets under an extractive QA setup.

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Sequence to Sequence Learning for Query Expansion

Cited by 4 publications

References 5 publications

Generation-Augmented Retrieval for Open-Domain Question Answering

Generation-Augmented Retrieval for Open-Domain Question Answering

SQE-GAN: A Supervised Query Expansion Scheme via GAN

Query Context Expansion for Open-Domain Question Answering

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