Abstract: As a fundamental NLP task, semantic role labeling (SRL) aims to discover the semantic roles for each predicate within one sentence. This paper investigates how to incorporate syntactic knowledge into the SRL task effectively. We present different approaches of encoding the syntactic information derived from dependency trees of different quality and representations; we propose a syntax-enhanced self-attention model and compare it with other two strong baseline methods; and we conduct experiments with newly published deep contextualized word representations as well. The experiment results demonstrate that with proper incorporation of the high quality syntactic information, our model achieves a new state-of-the-art performance for the Chinese SRL task on the CoNLL-2009 dataset.

Introduction: The task of semantic role labeling (SRL) is to recognize arguments for a given predicate in one sentence and assign labels to them, including “who” did “what” to “whom”, “when”, “where”, etc. Figure FIGREF1 is an example sentence with both semantic roles and syntactic dependencies. Since the nature of semantic roles is more abstract than the syntactic dependencies, SRL has a wide range of applications in different areas, e.g., text classification BIBREF0, text summarization BIBREF1, BIBREF2, recognizing textual entailment BIBREF3, BIBREF4, information extraction BIBREF5, question answering BIBREF6, BIBREF7, and so on. UTF8gbsn Traditionally, syntax is the bridge to reach semantics. However, along with the popularity of the end-to-end models in the NLP community, various recent studies have been discussing the necessity of syntax in the context of SRL. For instance, BIBREF8 have observed that only good syntax helps with the SRL performance. BIBREF9 have explored what kind of syntactic information or structure is better suited for the SRL model. BIBREF10 have compared syntax-agnostic and syntax-aware approaches and claim that the syntax-agnostic model surpasses the syntax-aware ones. In this paper, we focus on analyzing the relationship between the syntactic dependency information and the SRL performance. In particular, we investigate the following four aspects: 1) Quality of the syntactic information: whether the performance of the syntactic parser output affects the SRL performance; 2) Representation of the syntactic information: how to represent the syntactic dependencies to better preserve the original structural information; 3) Incorporation of the syntactic information: at which layer of the SRL model and how to incorporate the syntactic information; and 4) the Relationship with other external resources: when we append other external resources into the SRL model, whether their contributions are orthogonal to the syntactic dependencies. For the main architecture of the SRL model, many neural-network-based models use BiLSTM as the encoder (e.g., BIBREF10, BIBREF11, BIBREF12), while recently self-attention-based encoder becomes popular due to both the effectiveness and the efficiency BIBREF13, BIBREF14, BIBREF15. By its nature, the self-attention-based model directly captures the relation between words in the sentence, which is convenient to incorporate syntactic dependency information. BIBREF15 replace one attention head with pre-trained syntactic dependency information, which can be viewed as a hard way to inject syntax into the neural model. Enlightened by the machine translation model proposed by BIBREF16, we introduce the Relation-Aware method to incorporate syntactic dependencies, which is a softer way to encode richer structural information. Various experiments for the Chinese SRL on the CoNLL-2009 dataset are conducted to evaluate our hypotheses. From the empirical results, we observe that: 1) The quality of the syntactic information is essential when we incorporate structural information into the SRL model; 2) Deeper integration of the syntactic information achieves better results than the simple concatenation to the inputs; 3) External pre-trained contextualized word representations help to boost the SRL performance further, which is not entirely overlapping with the syntactic information. In summary, the contributions of our work are: We present detailed experiments on different aspects of incorporating syntactic information into the SRL model, in what quality, in which representation and how to integrate. We introduce the relation-aware approach to employ syntactic dependencies into the self-attention-based SRL model. We compare our approach with previous studies, and achieve state-of-the-art results with and without external resources, i.e., in the so-called closed and open settings.

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