Natural language sentence matching is the task of comparing two sentences and
identifying the relationship between this http URL has a wide range of applications
in natural language processing tasks such as reading comprehension, question
and answer systems. The main approach is to compute the interaction between
text representations and sentence pairs through an attention mechanism, which
can extract the semantic information between sentence pairs well. However,this
kind of method can not gain satisfactory results when dealing with complex
semantic features. To solve this problem, we propose a sentence matching method
based on deep encoding and interaction to extract deep semantic information. In
the encoder layer,we refer to the information of another sentence in the
process of encoding a single sentence, and later use a heuristic algorithm to
fuse the information. In the interaction layer, we use a bidirectional
attention mechanism and a self-attention mechanism to obtain deep semantic
information.Finally, we perform a pooling operation and input it to the MLP for
classification. we evaluate our model on three tasks: recognizing textual
entailment, paraphrase recognition, and answer selection. We conducted
experiments on the SNLI and SciTail datasets for the recognizing textual
entailment task, the Quora dataset for the paraphrase recognition task, and the
WikiQA dataset for the answer selection task. The experimental results show
that the proposed algorithm can effectively extract deep semantic features that
verify the effectiveness of the algorithm on sentence matching tasks.

通过深度编码和交互来提取深层语义信息的句子匹配方法，使用自注意机制和双向注意机制获得深层语义信息并进行分类，适用于文本蕴含识别、释义识别和答案选择，实验证明其可有效提取深层语义特征并验证其在句子匹配任务中的有效性。

DEIM：一种有效的句子匹配深度编码与交互模型

DEIM: An effective deep encoding and interaction model for sentence matching

Natural Language Sentence Matching (NLSM) has gained substantial attention
from both academics and the industry, and rich public datasets contribute a lot
to this process. However, biased datasets can also hurt the generalization
performance of trained models and give untrustworthy evaluation results. For
many NLSM datasets, the providers select some pairs of sentences into the
datasets, and this sampling procedure can easily bring unintended pattern,
i.e., selection bias. One example is the QuoraQP dataset, where some
content-independent naive features are unreasonably predictive. Such features
are the reflection of the selection bias and termed as the leakage features. In
this paper, we investigate the problem of selection bias on six NLSM datasets
and find that four out of them are significantly biased. We further propose a
training and evaluation framework to alleviate the bias. Experimental results
on QuoraQP suggest that the proposed framework can improve the generalization
ability of trained models, and give more trustworthy evaluation results for
real-world adoptions.

本文对自然语言句子匹配（NLSM）中可能出现的选择偏见进行了调查，并提出了一种训练和评估框架以减轻偏见带来的负面影响，实验结果表明该框架可以提高训练模型的泛化能力，并给出更加可信的评估结果。

自然语言句子匹配数据集的选择偏差探索和去偏方法

Selection Bias Explorations and Debias Methods for Natural Language  Sentence Matching Datasets

Natural language sentence matching is a fundamental technology for a variety
of tasks. Previous approaches either match sentences from a single direction or
only apply single granular (word-by-word or sentence-by-sentence) matching. In
this work, we propose a bilateral multi-perspective matching (BiMPM) model
under the "matching-aggregation" framework. Given two sentences $P$ and $Q$,
our model first encodes them with a BiLSTM encoder. Next, we match the two
encoded sentences in two directions $P \rightarrow Q$ and $P \leftarrow Q$. In
each matching direction, each time step of one sentence is matched against all
time-steps of the other sentence from multiple perspectives. Then, another
BiLSTM layer is utilized to aggregate the matching results into a fix-length
matching vector. Finally, based on the matching vector, the decision is made
through a fully connected layer. We evaluate our model on three tasks:
paraphrase identification, natural language inference and answer sentence
selection. Experimental results on standard benchmark datasets show that our
model achieves the state-of-the-art performance on all tasks.

提出一种双向多角度匹配（Bilateral Multi-Perspective Matching）模型，利用 BiLSTM 编码器实现 “匹配 - 汇聚” 框架下的多角度匹配，用于自然语言句子匹配，在对比实验中取得了和前沿技术同等水平的性能表现。