Interpretable Question Answering on Knowledge Bases and Text

Sydorova, Alona; Poerner, Nina; Roth, Benjamin

doi:10.18653/v1/p19-1488

Cited by 25 publications

(16 citation statements)

References 12 publications

(12 reference statements)

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“…In unsupervised approaches, many QA systems have relied on structured knowledge base (KB) QA. For example, several previous works have used ConceptNet (Speer et al, 2017) to keep the QA process interpretable (Khashabi et al, 2018b;Sydorova et al, 2019). However, the construction of such structured knowledge bases is expensive, and may need frequent updates.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering

Yadav

Bethard

Surdeanu

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Evidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of the corresponding QA method. We introduce a simple, fast, and unsupervised iterative evidence retrieval method, which relies on three ideas: (a) an unsupervised alignment approach to soft-align questions and answers with justification sentences using only GloVe embeddings, (b) an iterative process that reformulates queries focusing on terms that are not covered by existing justifications, which (c) a stopping criterion that terminates retrieval when the terms in the given question and candidate answers are covered by the retrieved justifications. Despite its simplicity, our approach outperforms all the previous methods (including supervised methods) on the evidence selection task on two datasets: MultiRC and QASC. When these evidence sentences are fed into a RoBERTa answer classification component, we achieve state-of-the-art QA performance on these two datasets.

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Section: Related Workmentioning

confidence: 99%

Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering

Yadav

Bethard

Surdeanu

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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“…The model proposed by Xiong et al (2019) contains a graph-attention based KB reader and a knowledge-aware text reader. Some other work focuses on retrieving a small graph that contains just the question-related information (Sun et al, 2019) and the interpretability of QA on KB and text (Sydorova et al, 2019). These methods lack considering the high-order relationship among the entities contained in the text.…”

Section: Related Workmentioning

confidence: 99%

Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion

Han¹,

Cheng²,

Wang³

2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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The incompleteness of knowledge base (KB) is a vital factor limiting the performance of question answering (QA). This paper proposes a novel QA method by leveraging text information to enhance the incomplete KB. The model enriches the entity representation through semantic information contained in the text, and employs graph convolutional networks to update the entity status. Furthermore, to exploit the latent structural information of text, we treat the text as hyperedges connecting entities among it to complement the deficient relations in KB, and hypergraph convolutional networks are further applied to reason on the hypergraph-formed text. Extensive experiments on the WebQuestionsSP benchmark with different KB settings prove the effectiveness of our model.

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“…Interpretability Interpretability is an important line of research recently (Jiang et al, 2019;Sydorova et al, 2019;Asai et al, 2020). The nearest neighbor approach (Simard et al, 1993) is appealing in that we can explicitly know which training example triggers each prediction.…”

Section: Discussion and Related Workmentioning

confidence: 99%

Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference

Zhang¹,

Hashimoto²,

Liu³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is attracting much attention to mitigate data scarcity, but OOS detection becomes even more challenging. In this paper, we present a simple yet effective approach, discriminative nearest neighbor classification with deep self-attention. Unlike softmax classifiers, we leverage BERTstyle pairwise encoding to train a binary classifier that estimates the best matched training example for a user input. We propose to boost the discriminative ability by transferring a natural language inference (NLI) model. Our extensive experiments on a large-scale multi-domain intent detection task show that our method achieves more stable and accurate in-domain and OOS detection accuracy than RoBERTa-based classifiers and embeddingbased nearest neighbor approaches. More notably, the NLI transfer enables our 10-shot model to perform competitively with 50-shot or even full-shot classifiers, while we can keep the inference time constant by leveraging a faster embedding retrieval model.

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Interpretable Question Answering on Knowledge Bases and Text

Cited by 25 publications

References 12 publications

Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering

Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering

Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion

Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference

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