Efficient Argument Structure Extraction with Transfer Learning and Active Learning (2022)

Abstract: The automation of extracting argument structures faces a pair of challenges on (1) encoding long-term contexts to facilitate comprehensive understanding, and (2) improving data efficiency since constructing high-quality argument structures is time-consuming. In this work, we propose a novel context-aware Transformer-based argument structure prediction model which, on five different domains, significantly outperforms models that rely on features or only encode limited contexts. To tackle the difficulty of data annotation, we examine two complementary methods: (i) transfer learning to leverage existing annotated data to boost model performance in a new target domain, and (ii) active learning to strategically identify a small amount of samples for annotation. We further propose model-independent sample acquisition strategies, which can be generalized to diverse domains. With extensive experiments, we show that our simple-yet-effective acquisition strategies yield competitive results against three strong comparisons. Combined with transfer learning, substantial F1 score boost (5-25) can be further achieved during the early iterations of active learning across domains.

## introduction identifying and understanding the argumentative discourse structure in text has been a critical task in argument mining @xcite @xcite . it plays an important role of discovering the central theses and reasoning process across a wide spectrum of domains, from formal text such as legal documents @xcite @xcite and scientific literature @xcite @xcite , to online posts and discussions @xcite @xcite @xcite peer review erulemaking online comment (1) i think this submission does not meet the community standard. consequently, our main goal is to design an easyto-use framework that can facilitate researchers and practitioners to build argument structure extraction models for new domains rapidly and accurately. to this end, we first propose a novel contextaware argument relation prediction model, which can be directly fine-tuned from pre-trained transformers @xcite . for a given proposition, the model encodes a broad context of neighboring propositions in the same document, and predicts whether each of them supports, attacks, or has no relation to the original one. by contrast, prior work only encodes pairwise propositions while ignoring contexts @xcite . moreover, while training on a large labeled corpus has become the de facto method for neural models, labeling argument structures is a laborious process even for experienced annotators with domain knowledge @xcite @xcite . our second goal is to investigate efficient model training, by using fewer samples for a new domain. we study the following two complementary techniques: (i) transfer learning (tl) adapts models trained on existing annotated data in a different domain, or leverages unlabeled in-domain data for better representation learning. (ii) active learning (al) strategically selects samples in the new domain based on a sample acquisition strategy with the goal of optimizing training performance. this process is often done in multiple rounds within a given budget @xcite . as pointed out by @xcite , model-specific selection methods may not generalize across successor models and domains. we thus design model-independent strategies to encourage the inclusion of unseen words, and sentences with discourse markers. both are easy to implement and incur little computation cost. we compare them with popular methods based on uncertainty @xcite and sample diversity @xcite . for experiments, we release ampere++ foot_0 , the first dataset in the peer review domain labeled with argument relations. our annotation process involves over 10 months of training and multi-round sessions with experienced annotators, finally yielding 3, 636 relations over 400 reviews originally collected in our prior work @xcite . it has the highest overall relation density and the most attack relations, compared to prior datasets ( drawing from extensive experiment results, we make the following observations: (1) our proposed model, which can encode longer contexts, yields better argument relation prediction results than comparisons or variants that operate over limited contexts ( §6.1). (2) tl substantially improves performance for target domains when less labeled data is available. for example, for echr and cdcp, using ampere++ as the source domain, with only half of the target domain training data, the model achieves better f1 scores than nontransferred model trained over the entire training set ( §6.2). this also highlights the value of our am-pere++ data. (3) among al methods, our newly proposed model-independent acquisition strategies yield competitive results against comparisons that require significantly more computations ( §6.3). (4) tl further improves all al setups and narrows the gaps among strategies ( §6.3). ## related work argument structure extraction. analyzing argumentation in natural language text has seen rapid growth @xcite @xcite ), yet the most challenging aspect of it is to extract the structures among diverse argument components. conceptually, the structure extraction model needs to address two subtasks: (1) determining which propositions are targeted (head detection), and (2) identifying the argumentative relations towards the head propositions. early work @xcite takes inspiration from discourse parsing. while practically argument relations can be dis-persed across the text, contrary to assumptions in common discourse theory @xcite . more recent work considers all pairwise combinations of propositions @xcite @xcite , which incurs expensive computations for long documents. our model encodes a sequence of propositions and extract their labels in one forward pass, leading to much reduced training and inference complexity while allowing access to more contexts. transfer learning for structured prediction. collecting human annotations for structured tasks is costly, especially when discourselevel understanding and domain expertise are required @xcite @xcite . it is thus desirable to reuse existing labels from a similar task, and transfer learning (tl) is often employed. it can be divided into two broad categories (pan and yang, 2009): (1) transductive approaches adapt models learned from a labeled source domain to a different target domain over the same task, and have shown promising results for discourse @xcite and argument @xcite ; accuosto and saggion, 2019) related tasks. (2) inductive methods aim to leverage unlabeled data, usually in the same domain as the target domain, and have gained popularity with the pre-training and fine-tuning paradigm using transformer models @xcite . we study both types in this work, with a particular focus on transductive approaches where the effect of different source domains are compared. active learning (al) has been explored in many nlp problems including named entity recognition @xcite ), text classification @xcite , and semantic parsing @xcite . unlike the traditional supervised setting where training data is sampled beforehand, al allows the learning system to actively select samples to maximize the performance, subject to an annotation budget @xcite . common al strategies are either based on model uncertainty @xcite , or promoting the diversity in sample distribution @xcite . however, both paradigms require coupling sampled data with a specific learned for each head proposition s j , we encode both the backward (purple) and forward (green) contexts. h j , the last layer states, represents proposition s j . h i , where i can be @xmath0, @xmath1, . . . , @xmath2, is concatenated with h j and fed into the pairwise output layer, to yield the probability of s j ← s i . ## argument relation prediction model task formulation. given a document that is segmented into a list of propositions, our task is to predict the existence of a support or attack link s j ← s i between propositions s i and s j . here the targeted proposition s j is the head, and s i is the tail. our end-to-end model considers all proposition pairs. we also consider a simplified setting, where head propositions are given a priori. a context-aware model. tokens. we use their last layer's states, denoted as h j , to represent s j . other propositions within the window defined by l then become candidates for tail propositions. after encoding, each tail candidate representation h i is concatenated with the head representation h j to form the input to the output layer, with the final prediction formulated as: ## active learning strategies one major goal of this work is to explore al solutions that can reduce the amount of samples for annotation, since labeling such a dataset can be the most laborious part of argument structure understanding. we consider a pool-based al scenario @xcite , where labels for the training set u are assumed to be unavailable initially. the learning procedure is carried out in t iterations. in the t-th iteration, b samples are selected using a given acquisition strategy. these samples are labeled and added into the labeled pool to comprise d t , on which a model m t is then trained. ## datasets and domains we experiment with five datasets from distinct domains, with key statistics listed in ## experiments and results in this section, we design experiments to answer the following questions. (1) to which degree is the context-aware model better at identifying argumentative relations ( §6.1)? (2) how much improvement can transfer learning (tl) make when different source domains are considered for a target domain ( §6.2)? (3) does unlabeled in-domain data help downstream tasks using self-supervised pretraining and inductive transfer learning ( §6.2)? (4) how do active learning (al) strategies perform on relation prediction and whether combining transfer learning leads to further performance boost ( §6.3)? evaluation is based on macro-f1 scores as done in prior work @xcite . for tasks without attack labels (echr and cdcp), the macro average is calculated over support and no-rel only, otherwise it is averaged over three classes. each setup is run five times with different random seeds, and the average scores on test sets are reported. implementation of our models is based on the transformer @xcite . our encoder is roberta-base @xcite , which has 12 layers with a hidden size of 768. we apply dropout @xcite with a probability of 0.1 for the output mlp layer. we use the adam optimizer (kingma and ba, 2015) with 16 sequences per batch. we hypertune our proposed argument relation prediction model with different number of maximum training epochs {5, 10, 15}, warmup steps {0, 1000, 5000}, learning rate {1e-5, 1e-6, 5e-5}, and scheduler {constant, linear}. the best validation result is achieved with 15 epochs, 5000 warmup steps, 1e-5 as learning rate, and the constant scheduler. we use this configuration for all model training experiments. each entry is averaged over five runs with different random seeds. the best result for each dataset is bolded. our context-aware model outperforms both baselines except for abstrct. the difference between head given and end-to-end is close, suggesting that the key challenge for structure extraction lies in relation prediction. our model performance improves when larger window size l is used. ## conclusion we present a simple yet effective framework for argument structure extraction, based on a contextaware transformer model that outperforms strong comparisons on five distinct domains, including our newly annotated dataset on peer reviews. we further investigate two complementary frameworks based on transfer learning and active learning to tackle the data scarcity issue. based on our extensive experiments, transfer learning from our newly annotated ampere++ dataset and self-supervised pre-training consistently yield better performance. our model-independent strategies approach popular model-based active learning methods.