What If Sentence-hood is Hard to Define: A Case Study in C hinese Reading Comprehension (2021)

Abstract: Machine reading comprehension (MRC) is a challenging NLP task for it requires to carefully deal with all linguistic granularities from word, sentence to passage. For extractive MRC, the answer span has been shown mostly determined by key evidence linguistic units, in which it is a sentence in most cases. However, we recently discovered that sentences may not be clearly defined in many languages to different extents, so that this causes so-called location unit ambiguity problem and as a result makes it difficult for the model to determine which sentence exactly contains the answer span when sentence itself has not been clearly defined at all. Taking Chinese language as a case study, we explain and analyze such a linguistic phenomenon and correspondingly propose a reader with Explicit Span-Sentence Predication to alleviate such a problem. Our proposed reader eventually helps achieve a new state-of-the-art on Chinese MRC benchmark and shows great potential in dealing with other languages.

## introduction machine reading comprehension (mrc) is a task that requires models to answer a question according to a given passage. this is a challenging task for it demands to carefully deal with all linguistic granularities from word, sentence to passage @xcite . for extractive mrc as the focus of this paper, the answer span has been shown mostly determined by key evidence linguistic units, in which it is a sentence in most cases @xcite . however, we recently found that sentences may be not clearly defined in many languages to different extents, so that this causes so-called location unit ambiguity problem to let model more difficultly determine which sentence exactly contains the answer span when sentence itself has not been clearly defined at all. in detail, sentence may include multiple clauses like english, or it consists of a series of sub-sentences like chinese, where all sub-sentences share the same subject, predicate or object @xcite . when a language has relatively strict grammar means to determine the boundaries of sentence constituents such as clauses or sub-sentences, it will facilitate mrc models to more conveniently focus on a certain range of text for finding answer span. otherwise, there comes an obvious so-called location unit ambiguity problem to hinder the performance of extractive mrc. in the following, we take chinese language as a case study to explain and analyze such a linguistic phenomenon and correspondingly find a solution. for the characteristics of chinese, "in terms of sentence structure, english is determined by rule, while chinese is determined by man" @xcite , that is, english focuses more on syntax while chinese focuses more on semantics. a full long english sentence has to be subject to strict grammatical means so that clauses can be clearly identified, while in chinese, such a long sentence may be written in a loose way, typically, whose subject may be conveniently omitted for all later sub-sentences, so that the boundaries between sentences and subsentences are blurred. as a result, there are more independent short sentences in chinese which may be equally written as a single grammar-rigorous long one in english @xcite @xcite . as shown in a chinese mrc example in figure 1 , the completely paraphrased sentence to answer the question is given in a series of short sub- ## our proposed model as shown in figure 2 , our proposed reader with explicit span-sentence predication (espreader) consists of three modules, that is prlm encoder, sentence-level self-attention layer and fusion crossattention layer. the details will be given below. ## conclusion this paper aims at addressing the newly discovered difficulty of the boundary ambiguity between sentences and sub-sentences, which exists in many languages to different extents and essentially limits the performance of span extraction mrc models, especially in chinese environment. we apply explicit span-sentence predication (esp) to enhance model's ability of precisely locating sentences containing the target span. our proposed model design is evaluated on chinese span extraction mrc benchmark, cmrc 2018. the experimental results show that our model significantly improves both em and f1 scores compared with strong baselines and helps achieve a new state-of-the-art performance. our method also shows generality and potential in dealing with other languages. this work highlights the research line of further improving challenging mrc by analyzing specific linguistics phenomena.