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.
翻译:许多 NLP 任务可被视为来自一组选项的选择问题, 例如分类任务、 多选择问题解答等。 文本要求(TE) 已被显示为处理这些选择问题的最先进( SOTA) 方法。 TE 将输入文本作为前提( P), 选项作为假设( H), 然后通过建模( P, H) 处理选择问题。 两个限制 : 首先, 配对型建模并不了解其他选项, 因为通过比较竞合候选人来决定最佳选项, 而其他选项不易理解; 第二, 配对型TE 的推论过程需要时间, 特别是当选项空间大时。 要处理这两个问题, TE 将输入文本文本文本作为前提化的文本( P), 然后将其他 k 选项作为当前( P, H) 建模( P, H) 模型来处理选择问题。 背景- TE 能够学习对 H的更可靠的决定, 因为它会考虑不同的背景。 其次, 我们加快了背景- TE, 与平行TE 一起来, 特别在可比较的选项中, 格式上, 我们的检测中, 的选项是平行- 。