CLaC at SemEval-2020 Task 5: Muli-task Stacked Bi-LSTMs

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 445–450 Barcelona, Spain (Online), December 12, 2020. 445 CLaC at SemEval-2020 Task 5: Muli-task Stacked Bi-LSTMs MinGyou Sung, Parsa Bagherzadeh, Sabine Bergler CLaC Labs Concordia University Montreal, Canada {mi sung, p bagher, bergler} @cse.concordia.ca Abstract We consider detection of the span of antecedents and consequents in argumentative prose a structural, grammatical task. Our system comprises a set of stacked Bi-LSTMs trained on two complementary linguistic annotations. We explore the effectiveness of grammatical features (POS and clause type) through ablation. The reported experiments suggest that a multi-task learning approach using this external, grammatical knowledge is useful for detecting the extent of antecedents and consequents and performs nearly as well without the use of word embeddings. 1 Introduction Conditional statements are of interest to investigations into the semantics of natural language text because they inform on factivity of statements as well as establish reasoning and argumentation in text. In formal logic, conditionals obey the principle of explosion (ex falso quod libed), and counterfactuals allow any conclusion. In language, however, assuming that facts had been different is frequently used to explore, for instance, causal relations relevant to the ongoing argument. Both cases differ significantly from basic assertions and flagging and classifying these very specific passages of text will enhance other semantic annotations. The basic structure of a conditional is thus found in many utterances, often as a way to specify presuppositions or assumptions for a given statement. Conditionals consist of two parts, the antecedent and the consequent, as illustrated in Example 1, where the antecedent (the if part) is underlined and the consequent spans the remainder of the sentence. Counterfactuals are conditionals where the antecedent does not hold true. Example 1 If there were peace, I wouldn’t spend another second here. While only few NLP systems attempt to model inference, a significant number is concerned with attributing degrees of factuality to different statements. Before conditional statements can be mined for their contribution to factivity judgments, they have to be detected. SemEval 2020 Subtask 5.2 (Yang et al., 2020) is concerned with identifying the span of antecedent and consequent clauses in text. The data samples consist largely of single sentences, but may involve several sentences. We stipulate that this is a mainly a structural task and experiment with the grammatical notion of clause boundaries. Encoding various clause types on top of POS tags and Glove Word Embeddings (WEs) (Pennington et al., 2014), we find that a clause type layer improves the performance of a baseline of only Glove WEs but barely improves on a two layer architecture encoding WEs and POS only. However, a two layer architecture encoding only POS and clause type shows competitive performance at a drastically reduced parameter space. Our system represented the median in the officially scored systems and demonstrates that simple grammatical notions can be stable contributors to this task. 2 System description We cast the task of detecting the spans of antecedent (A), consequent (C), and other text …

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