mlsanityData to 14 Oct 2026

Learning to Select from Multiple Options

arXiv 2212.00301 ↗First posted 1 Dec 2022cs.CLcited by 4

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Abstract (arXiv)

Many NLP tasks can be regarded as a selection problem from a set of options, such as classification tasks, multi-choice question answering, etc. Textual entailment (TE) has been shown as the state-of-the-art (SOTA) approach to dealing with those selection problems. TE treats input texts as premises (P), options as hypotheses (H), then handles the selection problem by modeling (P, H) pairwise. Two limitations: first, the pairwise modeling is unaware of other options, which is less intuitive since humans often determine the best options by comparing competing candidates; second, the inference process of pairwise TE is time-consuming, especially when the option space is large. To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling. Context-TE is able to learn more reliable decision for the H since it considers various context. Second, we speed up Context-TE by coming up with Parallel-TE, which learns the decisions of multiple options simultaneously. Parallel-TE significantly improves the inference speed while keeping comparable performance with Context-TE. Our methods are evaluated on three tasks (ultra-fine entity typing, intent detection and multi-choice QA) that are typical selection problems with different sizes of options. Experiments show our models set new SOTA performance; particularly, Parallel-TE is faster than the pairwise TE by k times in inference. Our code is publicly available at https://github.com/jiangshdd/LearningToSelect.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 32 of its 40 references that are in the map. First posted on arXiv 1 Dec 2022.

Builds on

32 of 40 references.

References · 32

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20 more
  1. 2019ConveRT: Efficient and Accurate Conversational Representations from Transformerscited by 95
  2. 2020DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialoguecited by 89
  3. 2018Ultra-Fine Entity Typingcited by 87
  4. 2021DocNLI: A Large-scale Dataset for Document-level Natural Language Inferencecited by 75
  5. 2021Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extractioncited by 73
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  7. 2015Learning Natural Language Inference with LSTMcited by 51
  8. 2020Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inferencecited by 47
  9. 2021Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuningcited by 46
  10. 2020Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Startcited by 44
  11. 2021Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Modelcited by 39
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  13. 2021Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and Systemcited by 30
  14. 2016Attentive Pooling Networkscited by 28
  15. 2020Example-Driven Intent Prediction with Observerscited by 26
  16. 2022Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learningcited by 25
  17. 2019Learning to Denoise Distantly-Labeled Data for Entity Typingcited by 24
  18. 2021Fine-grained Entity Typing via Label Reasoningcited by 22
  19. 2022Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inferencecited by 17
  20. 2017Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanismscited by 5

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Cited by year

Citing papers per year: 2022: 1; 2023: 3.

4 papers.

  1. 2023Zero-Shot Text Classification via Self-Supervised Tuningcited by 6
  2. 2022Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typingcited by 4
  3. 2023Dense Retrieval as Indirect Supervision for Large-space Decision Makingcited by 3
  4. 2023All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding Paradigmcited by 1