Inter-Weighted Alignment Network for Sentence Pair Modeling

Abstract

Sentence pair modeling is a crucial problem in the field of natural language processing. In this paper, we propose a model to measure the similarity of a sentence pair focusing on the interaction information. We utilize the word level similarity matrix to discover fine-grained alignment of two sentences. It should be emphasized that each word in a sentence has a different importance from the perspective of semantic composition, so we exploit two novel and efficient strategies to explicitly calculate a weight for each word. Although the proposed model only use a sequential LSTM for sentence modeling without any external resource such as syntactic parser tree and additional lexicon features, experimental results show that our model achieves state-of-the-art performance on three datasets of two tasks.

A Compare-Aggregate Model with Latent Clustering for Answer Selection

“…Then, these representations are compared. Finally, the results are aggregated to calculate the matching score between the question and the answer [1,9,12].…”

“…WikiQA [17] is an answer selection QA dataset constructed from real queries of Bing and Wikipedia. Following the literature [1,9], we use only questions that contain at least one correct answer among the list of answer candidates.…”

A Gated Self-attention Memory Network for Answer Selection

“…The WikiQA dataset (Yang et al, 2015) was constructed from real queries of Bing and Wikipedia. Following the literature (Yang et al, 2015;Bian et al, 2017;Shen et al, 2017), we removed all questions with no correct answers before training and evaluating answer selection models. Cooking Question: How do I prevent tomatoes from falling in a green salad?…”

“…Previous work on answer selection typically relies on feature engineering, linguistic tools, or external resources (Wang et al, 2007;Wang and Manning, 2010;Heilman and Smith, 2010;Yih et al, 2013;Yao et al, 2013). Recently, with the renaissance of neural network models, many deep learning based methods have been proposed to address the task (Tay et al, 2017b;Shen et al, 2017;Bian et al, 2017;Tymoshenko and Moschitti, 2018;Tay et al, 2018;Tayyar Madabushi et al, 2018;Yoon et al, 2019). They outperform traditional techniques.…”

The Context-Dependent Additive Recurrent Neural Net

“…c n , the goal is to identify positive sentences that contain the answer. Many researchers have investigated employing neural networks for this task (Rao The IWAN model proposed in (Shen et al, 2017) achieves state-of-the-art performance on the Clean version TrecQA dataset (Wang et al, 2007) for answer selection. In general, given two sentences, the model aims to calculate a score to measure their similarity.…”