Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization (2020)

Abstract: State-of-the-art argument mining studies have advanced the techniques for predicting argument structures. However, the technology for capturing non-tree-structured arguments is still in its infancy. In this paper, we focus on non-tree argument mining with a neural model. We jointly predict proposition types and edges between propositions. Our proposed model incorporates (i) task-specific parameterization (TSP) that effectively encodes a sequence of propositions and (ii) a proposition-level biaffine attention (PLBA) that can predict a non-tree argument consisting of edges. Experimental results show that both TSP and PLBA boost edge prediction performance compared to baselines.

## introduction argument mining, a research area that focuses on predicting argumentation structures in a text, has been receiving much attention. to date, efforts in argument mining were devoted to predicting tree arguments in which a claim proposition is represented as a root and premise propositions are represented as leaves. for example, @xcite introduced argument annotated essays (hereafter, essay), and researchers attempted to predict tree arguments in the corpus @xcite @xcite . however, these techniques lack the capability of dealing with more flexible arguments such as reason edges where a proposition can have several parents. to this end, @xcite provided a less restrictive argument mining dataset known as cornell erulemaking corpus (cdcp), which contains flexible edges (see @xcite , (b), and testimony (e) in figure 1 ). figure 2 shows a distribution of outgoing edges for essay and cdcp. propositions in cdcp have sparse connections, making the majority of propositions iso0 1 2 3 4 5 #outgoing edge 0 2000 4000 frequency essay cdcp figure 2: distribution of the outgoing edges (i.e., support/attack or reason/evidence relations) from a node (proposition) in essay and cdcp corpora in this paper, we present a novel model for nontree argument mining. different from the previous studies of @xcite ; @xcite , we focus on an effective encoding for the propositions and a graph-based non-tree argument parsing technique. given sentence or clause spans in an argument, our model jointly predicts proposition types for the spans, edges between the propositions and edge labels by employing following two architectures: -task-specific parameterization (tsp) is an effective encoding step for the proposition sequence. on top of a shared encoder, we prepare two dis-tinct attention-to-encoder layers to maintain taskspecific representations. one is for the proposition type, and the other for the edges (and their labels). tsp employs our expectation that edge-and proposition type-specific representations should be separately obtained. this is because representations of proposition types and edges are relatively less bonded when compared to the tree-structured essay where each premise proposition always has one outgoing edge. ## dataset we use cdcp @xcite with 731 arguments. the corpus provides five types of propositions (32 refer-ence, 746 fact, 1026 testimony, 2160 value and 815 policy), and two types of argumentative edges (1307 reason and 46 evidence). for example, fact poses a truth value that can be verified with objective evidence: that process usually takes as much as two years or more. cdcp also provides directed edges between propositions and edge label. a proposition i is reason for a proposition j if i provides rationale for j, or is evidence if it proves whether j is true or not. ## approach an overview of our proposed model is shown in figure 3 (right). we encode propositions by tsp, and use plba to obtain non-tree arguments. we use w t to denote the concatenation of t-th set of word features, each set consisting of a surface, a part-of-speech tag, a glove vector @xcite and an optional elmo vector @xcite . the input words for span i are fed into a bidirectional lstm: h start(i):end(i) = bilstm w start(i):end(i) . ## experiments following niculae et al. (2017), we evaluate the test set of cdcp that contains 973 propositions and 272 edges. f1 scores for the proposition type prediction and the edge prediction along with their average are used for the evaluations. for the edge labels, we only consider the classification of evidence rather than macro-averaged scores because labels are highly imbalanced. we calculate label scores on gold edges. ## results figure 4a shows that the edge scores for the non-multi-task model are significantly lower, while figure 4b shows that its type scores are barely affected. the result implies the edge task utilizes type information in the lower layer, but the type task is less dependent on edges. besides, the edge scores for the non-tsp model are worse, indicating that tsp is effective in obtaining a stable performance. the result implies that tsp acquires edge-specific representations independently from types. ## related work researchers in argument mining have been utilizing essay @xcite , a tree argument corpus. for example, @xcite employed integer linear programming. @xcite investigated argument mining as a dependency parsing problem with neural models. @xcite developed a pointer network architecture to predict edges. however, we cannot simply utilize them for non-tree arguments because these models were built upon the assumption that an argument forms a tree structure. non-tree arguments are relatively less emphasized. @xcite attempted to resolve the problem with a factor-based model. our study is primarily inspired by the semantic dependency parsing of @xcite and we predict the whole graph jointly. @xcite proposed a deep learning-based model that utilizes residual connections to predict proposition pair relations. ## conclusion this paper focused on non-tree argument mining. we provided an approach to effectively encode a proposition sequence and to predict non-tree edges. experimental results showed that our proposed model outperforms baselines. this paper demonstrated that we could successfully analyze more flexible structures in arguments. for future work, we aim to develop a universal model to handle both tree and non-tree arguments.