Abstract
We explore a novel approach for Semantic Role Labeling (SRL) by casting it as a sequence-to-sequence process. We employ an attention-based model enriched with a copying mechanism to ensure faithful regeneration of the input sequence, while enabling interleaved generation of argument role labels. We apply this model in a monolingual setting, performing PropBank SRL on English language data. The constrained sequence generation set-up enforced with the copying mechanism allows us to analyze the performance and special properties of the model on manually labeled data and benchmarking against state-of-the-art sequence labeling models. We show that our model is able to solve the SRL argument labeling task on English data, yet further structural decoding constraints will need to be added to make the model truly competitive. Our work represents the first step towards more advanced, generative SRL labeling setups.
Highlights
Semantic Role Labeling (SRL) is the task of assigning semantic argument structure to constituents or phrases in a sentence, to answer the question: Who did what to whom, where and when? This task is normally accomplished in two steps: first, identifying the predicate and second, labeling its arguments and the roles that they play with respect to the predicate
Our main contributions in this work are: (i) We propose a novel neural architecture for SRL using a seq2seq model enhanced with attention and copying mechanisms. (ii) We evaluate this model in a monolingual setting, performing PropBank-style SRL on standard English datasets, to assess the suitability of this model type for the SRL labeling task. (iii) We compare the performance of our model to state-of-the-art sequence labeling models, including detailed error analysis. (iv) We show that the seq2seq model is suited for the task, but still lags behind sequence labeling systems that include higher-level constraints
Due to its generative nature, many target sequences diverged from the source in both length and token sequences. This was expected, because the system has to learn to generate the labels at the correct time-step and to re-generate the complete sentence accurately. This is a disadvantage compared to the sequence labeling models where the words are already given
Summary
Semantic Role Labeling (SRL) is the task of assigning semantic argument structure to constituents or phrases in a sentence, to answer the question: Who did what to whom, where and when? This task is normally accomplished in two steps: first, identifying the predicate and second, labeling its arguments and the roles that they play with respect to the predicate. Semantic Role Labeling (SRL) is the task of assigning semantic argument structure to constituents or phrases in a sentence, to answer the question: Who did what to whom, where and when? Recent end-to-end neural models considerably improved the state-of-the-art results for SRL in English (He et al, 2017; Marcheggiani and Titov, 2017). Such models treat the problem as a supervised sequence labeling task, using deep LSTM architectures that assign a label to each token within the sentence. Since annotating SRL data in larger amounts is expensive, the use of a generative neural network model could be beneficial for automatically obtaining more labeled data in low-resource settings. The model that we present in this paper is a first step towards a joint label and language generation formulation for SRL, using the sequence-to-sequence architecture as a starting point
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