Spurious Correlations in Cross-Topic Argument Mining (2021)

Abstract: Recent work in cross-topic argument mining attempts to learn models that generalise across topics rather than merely relying on within-topic spurious correlations. We examine the effectiveness of this approach by analysing the output of single-task and multi-task models for cross-topic argument mining, through a combination of linear approximations of their decision boundaries, manual feature grouping, challenge examples, and ablations across the input vocabulary. Surprisingly, we show that cross-topic models still rely mostly on spurious correlations and only generalise within closely related topics, e.g., a model trained only on closed-class words and a few common open-class words outperforms a state-of-the-art cross-topic model on distant target topics.

## introduction when a sentiment analysis model associates the word shrek with positive sentiment @xcite , it relies on a spurious correlation. while the movie shrek was popular at the time the training data was sampled, this is unlikely to transfer across demographics, platforms and years. while there exists a continuum from sentiment words such as fantastic to spurious correlations such as shrek, with words such as hollywood or anticipation being perhaps in a grey zone, demoting spurious correlations is key to learning robust nlp models @xcite @xcite . this paper studies a similar problem in state-ofthe-art cross-topic argument mining systems. the task of argument mining is to recognise the existence of claims and premises in a text span. the all code will be publicly available at https:// github.com/terne/spurious_correlations_ in_argmin standard evaluation protocol is to evaluate argument mining systems across topics, i.e., on heldout topics, precisely to avoid over-fitting to a single topic @xcite @xcite . this study shows that despite this sensible cross-topic evaluation protocol, stateof-the-art systems nevertheless rely primarily on spurious correlations, e.g., guns (figure 1 ). these spurious correlations transfer across some topics in popular benchmarks, but only because the topics are closely related. ## argument mining we first describe the task of argument mining, focusing, in particular, on the subtle difference between argument mining ('this is an argument for or against x') and stance detection ('this is an expression of opinion for or against x'). both tasks are very relevant for social scientists, monitoring the dynamics of public opinion. still, whereas stance detection can be used to see what fractions of demographic subgroups are in favor of or against some topic, argument mining can be used to identify the arguments made for and against policies in political discussions. what is an argument? an argument is made up of propositions (claims), which are statements that are either true or false. traditionally, an argument must consist of at least two claims, with one being the conclusion (major claim) and at least one reason (premise) backing up that claim. some argument annotation schemes ask annotators to label premises and major claims separately @xcite . others simplify the task to identifying claim or claim-like sentences @xcite or to whether sentences are claims supporting or opposing a particular idea or topic @xcite . the resources used in our experiments below are of the latter type: sentences are labeled as arguments if they present evidence or reasoning in relation to a claim or topic and are refutable. the resources used in our experiments are annotated with arguments in the context of a particular topic, as well as the argument's polarity, i.e., what is annotated relates to stance. the key difference between the current task and stance detection is that arguments require the author to present evidence or reasoning for or against the topic. ## datasets the ukp sentential argument mining corpus (ukp) @xcite ) contains 25,492 sentences spanning eight controversial topics (abortion, cloning, death penalty, gun control, marijuana legalization, school uniforms, minimum wage and nuclear energy), each annotated at the sentence level as one of three classes; no argument, ar-gument against, and argument for. for example, a sentence about death penalty may not be arguing for or against death penalty (no argu-ment), may present an argument against having death penalty as a punishment for a severe crime (argument against), or may present an argument in favor of the same (argument for). the data is annotated such that the evaluation of a sentence (being an argument or not) is not strictly dependent on the topic. however, it should still be unambiguously supportive of or against a topic. claims will not be annotated as an argument unless they include some evidence or reasoning behind the claim; however, @xcite do find a few wrongly annotated sentences in this regard. the corpus comes with a fixed 70-10-20 split. the ibm debater argument search engine dataset (ibm) is from a larger dataset of argumentative sentences defined through query patterns by @xcite . we use only the 2,500 sentences that are gold labelled -with binary labels, where positive labels were given to statements that directly support or contest a topic. the sentences are from wikipedia articles and span 50 topics. since the authors used queries to mine the examples, the data is imbalanced (70% positive). we introduce a random 70-30 split. the argument extraction corpus (aq) @xcite contains 5,374 sentences annotated with argument quality on a continuous scale between 0 (hard to interpret the argument) and 1 (easy to interpret the argument). of the corpora included in our study, this differs most from the others; however, the topics included are controversial topics (gun control, gay marriage, evolution, and death penalty), similar to the ukp corpus. the sentences are partly from the internet argument corpus @xcite and partly from createdebate.com. we introduce a random 70-30 split. the vaccination corpus (vacc) was presented in @xcite and consists of 294 documents from online debates on vaccination with marked claims. a claim is defined as opinionated statements wrt. vaccination. for our purpose, we split the documents into sentences (23,467). we use binary labels (claim or not) and introduce a random 70-10-20 split. ## experimental setup we now describe our learning architecture, an almost out-of-the-box application of the mt-dnn architecture in @xcite . it is a strong model that achieves a better performance than previously reported across the benchmarks. the mt-dnn model of @xcite combines the pre-trained bert architecture with multitask learning. the model can be broken up into shared layers and task-specific layers. the shared layers are initialised with the pre-trained bert base model @xcite . we add a taskspecific output layer for each task and update all model parameters during training with adamax. the task-specific layers are logistic regression classifiers with softmax activation, minimising crossentropy loss functions for classification tasks or mean squared error for regression tasks. if we only have a single output layer, we refer to the architecture as single-task dnn (st-dnn) rather than mt-dnn. we train all models over 10 epochs with a batch size of 5 for feasibility and otherwise use default hyperparameters. following @xcite , we iteratively combine the training and validation data from seven of the eight topics of the ukp corpus for training and parameter tuning and use the test data of the held-out topic for testing. we firstly treat the task as a single-sentence classification task and train an st-dnn with the bert-base model as shared layers. since @xcite argues multi-task learning effectively reduces sensitivity to spurious correlations, we experiment with mt-dnn models based on different data and task combinations: for each auxiliary dataset (ibm, aq, and vacc), we train an mt-dnn model with the ukp corpus as one task and the auxiliary data as another task. we denote the mt-dnn models as follows: mt-dnn+ibm refers to a model trained with the ibm data as an auxiliary claim classification task; mt-dnn+aq is trained with aq as an auxiliary regression task; mt-dnn+vacc is trained with vacc data as an auxiliary claim classification task; mt-dnn+aq+ibm+vacc is our largest model trained with all auxiliary tasks. topic-mt-dnn provides us with an upper bound: in this setting, all topics are used in training and tuning, including the target topic, as eight separate tasks. ## analysis we evaluate the models on the ukp corpus using the cross-topic evaluation protocol of @xcite ) -training with seven topics and testing on a held-out topic. we report the average macro f 1 across five random seeds. @xcite , unstable @xcite and very sensitive to the width of the kernel used to assign weights to input example perturbations @xcite , an increasing number of features also increases weight instability @xcite , and @xcite argues that with sparse data, sampling is insufficient. @xcite argues the specific sampling technique is suboptimal. since we use aggregate lime statistics across hundreds of data points, these weaknesses should have limited impact on our results; lime remains a de facto standard, and most alternatives suffer a) challenge examples for an initial qualitative error analysis, 19 short text pieces are taken from exercises made by jon m. young for his critical thinking course at fayetteville state university. 34 of these, the first six are examples of sentences that comprise an argument or not, and if they do, the conclusions and premises have been annotated by young. the last 13 examples are from exercises where we annotated the correct answers. we contrast the lime analyses of the predictions of our best performing model, i.e. mt-dnn+vacc+ibm+aq, as well as our st-dnn baseline. 5 an example of the lime explanations can be seen in figure 2 . the remaining lime explanations are in the appendix in figures 4 5 6 7 . out of the 19 examples, seven were incorrectly classified by our best model. common to these misclassified examples is either a rather uncontroversial, everyday topic (4c, 4g, 5e) or a very informative language (4h, 5g, 5h). since the model was mainly trained on controversial topics, it is not surprising that these uncontroversial cases make the model misstep. while this is a tiny sample, these incorrect classifications do suggest that our models do not transfer well to any topic, possibly indicating they rely more on topic words than on from similar weaknesses or are prohibitively costly to run. topic argument words topic words stance words other abortion if, that, for abortion, life, women, woman, human, pregnancy, unborn right, legal, hates the , is, to, in, it, be cloning would, will, if, could, potential cloning, clone, cloned, genetic not, no, abnormalities the , to, is, it, have, be, do death penalty would, if death, penalty, punishment, killing, crime not, murder, murderers the , to, in, is, of, are, people, it gun control gun, guns, criminals, background, checks, disarm, arms, armed no, safer, right, more, not, abiding a , the, are, and, in, is marijuana legalization would marijuana, use, effects, legalizing, legalization, drug, prohibition, drugs no, not, more, abuse, costs is , the, are, it school uniforms if, but uniforms, uniform, school, students, clothing, wears not, less, improve, decreased, uncomfortable, costs to , can, it, without minimum wage would, that, if wage, minimum, workers, wages, living, jobs, hour cost, more, no, many the , it, is, are, can nuclear energy that, if, for nuclear, power, energy, reactors, plants, waste, chernobyl, fuel safety, less is , the, to, has, can, it specifically, and to better understand the distribution of word types, we divide the top 20 words into four categories: argument words, topic words, stance words, and other. we define argument words as words that likely appear when present-ing claims, independent on the topic, including markers of evidence and reasons such as "if", "that" and "because" and similar lexical indicators based on @xcite . contrary to argument words, we define topic words as words that have no relation to the act of presenting an argument but are clearly related to the specific topic, e.g., nouns or verbs frequently used when debating or merely describing the topic. lastly, we define stance words as opinionated words that express a stance toward a topic (but is not only used in the context of arguments, i.e., presenting evidence). examples include describing death penalty as "murder" or school uniforms as "uncomfortable". three annotators agreed on the classification. words that did not fit our scheme were categorised as other. note that we do not normalize the accumulated lime weights by word frequency, which favors frequent words. when normalising the weights, our models also rely heavily on low-frequency stance words and for all topics, except cloning, there are many topic words among the top 20. highfrequency words (as well as most argument words) are naturally ranked much lower after normalisation. stance words are, of course, not spurious for our three-way classification problem, but a near dis-6 top 20 words along with their frequency and lime weights are provided at github.com/terne/ spurious_correlations_in_argmin/top_ words appearance of argument words in the normalized top 20 suggests our models are unlikely to capture low-frequency argument markers. c) how much weight do our models attribute to open class words, and does multi-task learning move emphasis to closed-class items? multitask learning is a regularization technique @xcite and may, as suggested by @xcite , reduce the extent to which our models rely on spurious correlations, which tend to be open class words. to compare the weight attributed to open-class words, across single-task and multi-task models, we define a score reflecting the weight put on open class words in a sentence: for each word in the sentence, we consider the maximum lime weight of the two weights towards the argument classes argument against and argument for. we then take the sum of lime weights put on open class words, normalised by the total sum of weights, and divide the normalised weight by the sentence fraction of open-class words. to explore whether removing open class words may improve generalization to more distant topics, we test the constrained models on the test sets of vacc and ibm. while the ukp dataset has three classes, the evaluation datasets have two. we, there-7 it is worth noting that the set of 67 common open class words above reflects that some words common across topics are in fact of an argumentative nature, with verbs such as "said", "find" and "found" that are often used for referencing sources when providing reasons for claims. we inspected common words among the highest-ranking open class words. we found that very few highly weighted words transfer across more than a few topics, e.g. even at the top 200 level, only one word, namely cost, transfer across four, i.e. half, of the topics. model ibm vacc st-dnn .656 .504 closed+shared .670 .569 supervised (upper bound) mt-dnn+vacc+ibm+aq .813 .856 feature analysis in deep neural networks feature analysis in deep neural networks is not straightforward but, by now, several approaches to attribute importance in deep neural networks to features or input tokens are available. one advantage of lime is that it can be applied to any model posthoc. other approaches for interpreting transformers, specifically, focus on inspections of the attention weights @xcite and vector norms @xcite . ## conclusion we have shown that cross-topic evaluation of argument mining is insufficient to prevent models from relying on spurious features. many of the spurious correlations that our models rely on are shared across some pairs of ukp topics but fail to generalise to distant topics (ibm and vacc). this shows cross-topic evaluation can encourage learning from signals, rather than spurious features; the problem with the protocol in @xcite is using multiple source topics. when using multiple source topics for training (and if the annotation relies on arguments being related to these topics), the models may overly rely on features that are frequent in debates of these topics but are not related to the forming of an argument and hence do not generalise well to unseen topics. the variance in cross-topic performance may be explained by some topic words transferring across a few topics, since the large variance disappears when removing open-class words. we propose evaluating on more distant held-out topics or simply considering the worst-case performance across all pairs of topics to estimate real-world out-of-topic performance. (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l)