Neural text matching models have been widely used in community question
answering, information retrieval, and dialogue. However, these models designed
for short texts cannot well address the long-form text matching problem,
because there are many contexts in long-form texts can not be directly aligned
with each other, and it is difficult for existing models to capture the key
matching signals from such noisy data. Besides, these models are
computationally expensive for simply use all textual data indiscriminately. To
tackle the effectiveness and efficiency problem, we propose a novel
hierarchical noise filtering model, namely Match-Ignition. The main idea is to
plug the well-known PageRank algorithm into the Transformer, to identify and
filter both sentence and word level noisy information in the matching process.
Noisy sentences are usually easy to detect because previous work has shown that
their similarity can be explicitly evaluated by the word overlapping, so we
directly use PageRank to filter such information based on a sentence similarity
graph. Unlike sentences, words rely on their contexts to express concrete
meanings, so we propose to jointly learn the filtering and matching process, to
well capture the critical word-level matching signals. Specifically, a word
graph is first built based on the attention scores in each self-attention block
of Transformer, and key words are then selected by applying PageRank on this
graph. In this way, noisy words will be filtered out layer by layer in the
matching process. Experimental results show that Match-Ignition outperforms
both SOTA short text matching models and recent long-form text matching models.
We also conduct detailed analysis to show that Match-Ignition efficiently
captures important sentences and words, to facilitate the long-form text
matching process.

提出一种层次噪声过滤模型 Match-Ignition，通过将 PageRank 算法嵌入 Transformer 并在匹配过程中在句子和单词级别识别和过滤噪声信息，以提高长型文本匹配问题的有效性和效率。