CL a C at S em E val-2020 Task 5: Muli-task Stacked B i- LSTM s (2020)

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.

## 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 @xcite 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) @xcite , 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. ## system description we cast the task of detecting the spans of antecedent (a), consequent (c), and other text (o) as a sequence labeling task. for an input sequence of n tokens s @xmath0, w i 2 , . . . , w in , we predict a sequence of this work is licensed under a creative commons attribution 4.0 international licence. licence details: http:// creativecommons.org/licenses/by/4.0/ . preprocessing we define a strict input mapping f between character labels and token labels, with the corresponding output mapping trivially maps the label of a token to all its constituent characters. ## results we divide the original training set into training (3251 samples) and validation (300 samples) sets. we present here only a small subset of extensive ablation on our variants. note that the analysis presented here refers to our experiments on the validation data only. table entries for test set performance are added here only to contextualize the development results. single layer baselines our first observation is that when used in a single layer architecture, pos1 trails our best three layer systems by less than .05 in f1 measure and less than 10 exact matches. pos2 outperforms glove we as a single layer baseline in f1, if not in exact matches, see three layer architectures the three layer architectures shown in we+pos1+cl1-1 is our submitted model (highlighted in ## conclusion our goal was to test the possibility of grammatical information to improve exact matches of antecedent and consequent span detection. ablation studies show that grammatical features by themselves form a solid baseline in a two layer bi-lstm architecture and dramatically reduce the parameter space for the task. our experiments demonstrate that multi-task stacked bi-lstm models can effectively super-encode the grammatical features pos and clause type, improving performance for both f1 and exact match scores. for this task, the difference in outcome barely justifies the increase in complexity for three layers. however, the combination results in stable systems that operate at the precision-recall break even point and that (slightly) outperform single and two layer models. they form thus a promising basis for semantically more complex tasks.