  Constituency parsing is a fundamental and important task for natural language
understanding, where a good representation of contextual information can help
this task. N-grams, which is a conventional type of feature for contextual
information, have been demonstrated to be useful in many tasks, and thus could
also be beneficial for constituency parsing if they are appropriately modeled.
In this paper, we propose span attention for neural chart-based constituency
parsing to leverage n-gram information. Considering that current chart-based
parsers with Transformer-based encoder represent spans by subtraction of the
hidden states at the span boundaries, which may cause information loss
especially for long spans, we incorporate n-grams into span representations by
weighting them according to their contributions to the parsing process.
Moreover, we propose categorical span attention to further enhance the model by
weighting n-grams within different length categories, and thus benefit
long-sentence parsing. Experimental results on three widely used benchmark
datasets demonstrate the effectiveness of our approach in parsing Arabic,
Chinese, and English, where state-of-the-art performance is obtained by our
approach on all of them.
