  Semantic text matching models have been widely used in community question
answering, information retrieval, and dialogue. However, these models cannot
well address the long-form text matching problem. That is because there are
usually many noises in the setting of long-form text matching, and it is
difficult for existing semantic text matching to capture the key matching
signals from this noisy information. Besides, these models are computationally
expensive because they simply use all textual data indiscriminately in the
matching process. To tackle the effectiveness and efficiency problem, we
propose a novel hierarchical noise filtering model in this paper, namely
Match-Ignition. The basic 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 the sentence is the basic unit of a long-form text, so we directly use
PageRank to filter such information, based on a sentence similarity graph.
While words need to rely on their contexts to express concrete meanings, so we
propose to jointly learn the filtering process and the matching process, to
reflect the contextual dependencies between words. Specifically, a word graph
is first built based on the attention scores in each self-attention block of
Transformer, and keywords 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
traditional text matching models for short text and recent long-form text
matching models. We also conduct detailed analysis to show that Match-Ignition
can efficiently capture important sentences or words, which are helpful for
long-form text matching.
