WVOQ at S em E val-2021 Task 6: BART for Span Detection and Classification (2021)

Abstract: Simultaneous span detection and classification is a task not currently addressed in standard NLP frameworks. The present paper describes why and how an EncoderDecoder model was used to combine span detection and classification to address subtask 2 of SemEval-2021 Task 6.

## introduction task 6 of semeval-2021 studies the detection of persuasion techniques @xcite . the task considers english language memes, which in subtasks are to be classified, divided into classified fragments having a begin and end, and classified when text is combined with images. of the three subtasks described in the paper, the present paper primarily addresses resolving subtask 2: given only the "textual content" of a meme, identify which of the 20 techniques are used in it together with the span(s) of text covered by each technique. this is a multilabel sequence tagging task. the figure below illustrates span detection and classification for three technique classes for a meme. in the above figure the ellipsis at the end of the sentence denotes the continuation of the second sentence. note that loaded language and name calling both apply to the same span, that is, the word "crackhead". note furthermore, that the span for smears overlaps with both of these spans and ranges over more than one sentence. the present paper describes a novel approach to resolving these requirements by generating xmllike start and end tokens to delineate spans. the following illustrates this for the message in figure 1 . it attained an f1 score on the test set that is about in the middle of the baseline and the highest ranking score. the choice of this approach of generating markup to identify spans was made on the basis that it was technically possible, easily understandable at a behavioral level of input and output, and using a model that is pre-trained for dealing with spans. the aim was not so much to attain the highest score as to explore how effective this approach is in a proof-of-concept and what problems need to be overcome to bring it to good performance. foot_0 2 background propaganda messages are constructed using specific rhetorical techniques. the current task is to identify within a message in what fragment a particular technique is invoked. a simpler task is to identify fragments of a message in which any propaganda technique is used. effectively, this comes down to classifying any part of a message as being either propaganda or not. it is a sequence labeling problem that can be resolved for example using a bio tagging format, where bio stands for begin, inside, and outside. to classify a span of tokens as propaganda we can use b-prop to designate the begin of the span, i-prop to indicate the token being inside the earlier begun span, and o to designate a token not being part of a span. @xcite . for our case this approach can be extended by adding new labels for each technique, e.g. ## results the system's f1 score of 0.268 on subtask 2 on the test set scores about in the middle between the baseline and the highest ranking score. looking at errors we made the following observations: ## conclusion the described system for span detection and simultaneous classification offers a proof-of-concept for a novel approach to sequence tagging based on generating a version of a message with markup for labels. its f1 score on the leaderboard is in the middle between the baseline and the top score. drawback of the approach is that two types of systemic errors are introduced: tags lacking a matching tag, and tokens generated that are not in the original message. these are not resolved by fine-tuning the model and they cannot be addressed with the standard configuration parameters of message generation in the sequence-to-sequence model. future research should aim at resolving these systemic errors. matching tags could be addressed through changes in the decoder's generation algorithm. having the tokens in the output be the same as those in the input could be improved by amending the loss function for fine-tuning training of the model. only when these two issues are resolved will further optimization of the approach be worth investing effort in.