Learning to Select from Multiple Options

TL;DR

This paper introduces Context-TE and Parallel-TE models that improve selection tasks in NLP by considering multiple options simultaneously, leading to better accuracy and faster inference compared to traditional pairwise methods.

Contribution

The paper proposes a novel contextualized TE model and a parallel decision model that enhance selection accuracy and significantly speed up inference in NLP tasks.

Findings

Context-TE improves decision reliability by considering multiple options.

Parallel-TE achieves k times faster inference than pairwise TE.

Models set new SOTA performance on multiple selection tasks.

Abstract

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.