Abstract: Semantic role labeling (SRL) is to recognize the predicate-argument structure of a sentence, including subtasks of predicate disambiguation and argument labeling. Previous studies usually formulate the entire SRL problem into two or more subtasks. For the first time, this paper introduces an end-to-end neural model which unifiedly tackles the predicate disambiguation and the argument labeling in one shot. Using a biaffine scorer, our model directly predicts all semantic role labels for all given word pairs in the sentence without relying on any syntactic parse information. Specifically, we augment the BiLSTM encoder with a non-linear transformation to further distinguish the predicate and the argument in a given sentence, and model the semantic role labeling process as a word pair classification task by employing the biaffine attentional mechanism. Though the proposed model is syntax-agnostic with local decoder, it outperforms the state-of-the-art syntax-aware SRL systems on the CoNLL-2008, 2009 benchmarks for both English and Chinese. To our best knowledge, we report the first syntax-agnostic SRL model that surpasses all known syntax-aware models.

Introduction: This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://creativecommons.org/licenses/by/4.0/ Semantic role labeling (SRL) is a shallow semantic parsing, which is dedicated to identifying the semantic arguments of a predicate and labeling them with their semantic roles. SRL is considered as one of the core tasks in the natural language processing (NLP), which has been successfully applied to various downstream tasks, such as information extraction BIBREF0 , question answering BIBREF1 , BIBREF2 , machine translation BIBREF3 , BIBREF4 . Typically, SRL task can be put into two categories: constituent-based (i.e., phrase or span) SRL and dependency-based SRL. This paper will focus on the latter one popularized by CoNLL-2008 and 2009 shared tasks BIBREF5 , BIBREF6 . Most conventional SRL systems relied on sophisticated handcraft features or some declarative constraints BIBREF7 , BIBREF8 , which suffers from poor efficiency and generalization ability. A recently tendency for SRL is adopting neural networks methods attributed to their significant success in a wide range of applications BIBREF9 , BIBREF10 . However, most of those works still heavily resort to syntactic features. Since the syntactic parsing task is equally hard as SRL and comes with its own errors, it is better to get rid of such prerequisite as in other NLP tasks. Accordingly, marcheggiani2017 presented a neural model putting syntax aside for dependency-based SRL and obtain favorable results, which overturns the inherent belief that syntax is indispensable in SRL task BIBREF11 . Besides, SRL task is generally formulated as multi-step classification subtasks in pipeline systems, consisting of predicate identification, predicate disambiguation, argument identification and argument classification. Most previous SRL approaches adopt a pipeline framework to handle these subtasks one after another. Until recently, some works BIBREF12 , BIBREF13 introduce end-to-end models for span-based SRL, which motivates us to explore integrative model for dependency SRL. In this work, we propose a syntactic-agnostic end-to-end system, dealing with predicate disambiguation and argument labeling in one model, unlike previous systems that treat the predicate disambiguation as a subtask and handle it separately. In detail, our model contains (1) a deep BiLSTM encoder, which is able to distinguish the predicates and arguments by mapping them into two different vector spaces, and (2) a biaffine attentional BIBREF14 scorer, which unifiedly predicts the semantic role for argument and the sense for predicate. We experimentally show that though our biaffine attentional model remains simple and does not rely on any syntactic feature, it achieves the best result on the benchmark for both Chinese and English even compared to syntax-aware systems. In summary, our major contributions are shown as follows:

Question: Was the degree of offensiveness taken as how generally offensive the text was, or how personally offensive it was to the annotator?
Answer: Personal thought of the annotator.