End-to-End Argument Mining as Biaffine Dependency Parsing (2021)

Abstract: Non-neural approaches to argument mining (AM) are often pipelined and require heavy feature-engineering. In this paper, we propose a neural end-to-end approach to AM which is based on dependency parsing, in contrast to the current state-of-the-art which relies on relation extraction. Our biaffine AM dependency parser significantly outperforms the state-of-the-art, performing at F1 = 73.5% for component identification and F1 = 46.4% for relation identification. One of the advantages of treating AM as biaffine dependency parsing is the simple neural architecture that results. The idea of treating AM as dependency parsing is not new, but has previously been abandoned as it was lagging far behind the state-of-the-art. In a thorough analysis, we investigate the factors that contribute to the success of our model: the biaffine model itself, our representation for the dependency structure of arguments, different encoders in the biaffine model, and syntactic information additionally fed to the model. Our work demonstrates that dependency parsing for AM, an overlooked idea from the past, deserves more attention in the future.

## introduction people often hold different opinions about the same thing. one very common way to express one's opinions is to construct an argument @xcite . to help people efficiently understand different opinions and their reasoning embedded in arguments, it is necessary to develop systems that can automatically analyse structures of arguments. an emerging research field called argumentation mining or argument mining (am) @xcite addresses this problem. to analyse the structure of arguments, am typically proposes four subtasks: 1) component segmentation, i.e., cutting a raw sequence into text spans that are either argumentative or nonargumentative segments (only argumentative segments are called argument components); 2) component classification, i.e., labelling each argument components with a tag in a pre-defined scheme, (e.g., "premise" or "claim"); 3) relation detection, i.e., deciding if two argument components are directly related; and 4) relation classification, i.e., categorizing a detected relation into a class in a predefined scheme, (e.g., "attack" or "support") @xcite @xcite @xcite . the first two subtasks are often referred together as component identification, and the last two as relation identification. an example of the argument structure is illustrated in figure 1 . we see that the "rain" component acts as the premise, supporting the claim of "beautiful". some of the am approaches that try to solve all four subtasks use a pipelined architecture. independent models are first trained for each subtask; the final results are then achieved by using model ensemble methods such as integer linear programming @xcite . like many other pipelined approaches, they often suffer from error propagation. moreover, many of those models are rule-based or featurebased @xcite , which require extensive manual efforts and are not flexible or robust in cross-domain scenarios. as a result, neural end-to-end approaches are desirable for am. however, it is difficult to jointly model all am subtasks within one single neural network, because component segmentation and figure 1 : an example argument structure for "cambridge is beautiful, because it rains a lot." as far as we know, @xcite present the first study on neural end-to-end am that addresses all subtasks in one model. they make a comprehensive comparison of methods, among which a model originally proposed for extracting entities and relations (lstm-er) achieves the best performance on a popular am benchmark @xcite , far outperforming one that uses a dependency parsing (dp) approach. in this paper, we propose another neural endto-end approach for the full am task, called biaffine dependency parsing for argument mining (bipam). in our approach, am is formalised as a dp problem, which we model with a modified biaffine neural network @xcite , using our own dependency representation for arguments. our representation, like the one in @xcite , also unifies all am subtasks under a token-level framework, so that they can be modelled with one single neural network. the biaffine parser in our approach mainly consists of a neural encoder to extract contextualized features of each token in a sequence, and a biaffine classifier that decides which relation holds between any two tokens, and returns a directed acyclic graph (dag) expressing this information. compared with the dp approach in @xcite , our model performs at a much higher performance rate. we argue that this is mainly due to the fact that our biaffine model is more powerful in modelling am-style dependency structures, and also due to other factors such as our dependency representation, which seems closer aligned with linguistic intuitions. @xcite may have discredited the dp approach altogether due to its unsatisfactory results in their setting, but our work reaffirms its potential for am, given a more powerful parser and a better dependency representation. experiments show that our approach also outperforms the best-performing approach (lstm-er) in @xcite and thus achieves a new state-of-the-art. compared with lstm-er, our biaffine parser is conceptually and structurally simpler, and our approach is more general because the output of our biaffine parser is a dag instead of a tree; this is required by several argument schemes which go beyond trees @xcite . our main contributions are as follows: our work is closely related to existing approaches framing am as dp and to research on neural endto-end am. the essential aim of am is to analyse the structure of arguments. most argument schemes @xcite @xcite represent argument structures as trees or dags. similar structures also exist in syntactic and semantic parsing, and can be efficiently analysed with existing dependency parsers @xcite @xcite . however, unlike syntactic or semantic parsing, dependency structures in am operate at the segment-level, not the tokenlevel. to apply existing dp techniques to am, one can either ignore the component segmentation task, only working on already segmented text, or one can convert segment-level dependencies to token-level dependencies. morio et al. (2020) take the first approach. in their work, the input is manually segmented, with argumentative and non-argumentative segments already given. they use task-specific bilstms to encode those segments, and a biaffine dependency parser @xcite with minor alternation to classify argument components and their relations. they report satisfactory results for component classification and relation identification on the cornell erulemaking corpus (cdcp) (niculae et al., 2017; park and cardie, 2018). in contrast, @xcite take the second approach, taking raw text as input so that the component segmentation task is addressed. they design a dependency representation for argument structures, in which dependencies are represented at the token-level, with all edges pointing from parent to child foot_0 . they have evaluated feature-based parsers @xcite and neural parsers @xcite , both of which show unsatisfactory performance. as a result, they discarded dp as inferior. our work is similar to @xcite in that we also use a modified biaffine dependency parser to model dependencies in argument structures, but similar to @xcite in that we model am as a full task starting from tokens, not segments. besides dp, @xcite also study other approaches to neural end-to-end am, including sequence tagging @xcite , multi-task tagging @xcite , and relation extraction @xcite . among them, the lstm-er model for relation extraction performs the best. however, it requires an additional syntactic parser to produce syntactic dependency trees for the model input. moreover, the tree-structured lstm module used to encode those syntactic dependency trees is both conceptually and structurally complicated. in contrast, our proposed approach does not require such syntactic information to break the state-of-the-art (although we will show that it can be enhanced if this information is provided), and the architecture of our biaffine dependency parser is both simpler and more general than that of the lstm-er. ## proposed approach we propose to formalise am as dp, using a modified biaffine dependency parser, which outputs dependency graphs. to make this work, we first design a dependency representation for arguments which unifies all subtasks of am under a tokenlevel framework. ## experiments we conduct experiments on a benchmark for am to evaluate the performance of our model. ## conclusion in this paper, we solve argument mining (am) as a neural end-to-end dependency parsing (dp) problem. as a non-pipelined approach, our solution is free from error propagation. we have demonstrated that the biaffine dependency parser from @xcite can act as a powerful am parser, under the right circumstances. we use bert as the encoder, feed syntactic information into the model in addition to raw text, and propose a novel, efficient and linguistically plausible representation for the dependency structure of arguments. our model significantly outperforms current state-of-the-art in neural end-to-end am, performing at f 1 = 73.5% for component identification and 46.4% for relation identification. our research suggests that dp, which has been prematurely abandoned in the past, is actually a very promising approach for am. through an ablation study, we find that both the modified biaffine parser and the dependency representation contribute to the performance improvement of bipam.