The workweek is the best time to start a family – A Study of GPT -2 Based Claim Generation (2020)

Abstract: Argument generation is a challenging task whose research is timely considering its potential impact on social media and the dissemination of information. Here we suggest a pipeline based on GPT-2 for generating coherent claims, and explore the types of claims that it produces, and their veracity, using an array of manual and automatic assessments. In addition, we explore the interplay between this task and the task of Claim Retrieval, showing how they can complement one another.

## introduction argument mining had traditionally focused on the detection and retrieval of arguments, and the classification of their types and of the relations among them. recently, there has been growing interest in argument synthesis. here we suggest a pipeline for addressing this task relying on the gpt-2 language model @xcite , examine how it can be enhanced to provide better arguments, and analyze the types of arguments being produced. specifically, we are interested in claim generation, where the input is a debate topic, phrased as a proposed policy, and the output is a concise assertion with a clear stance on this topic. we start by fine-tuning gpt-2 on a collection of topics and associated claims. since several such datasets are available, we examine which of them tend to yield better claims, and observe that merging all such sources together does not necessarily yield better results. in addition, we explore two ways in which context can be added to the generation process, beyond providing the topic itself: (i) framing the topic with the first sentence from its corresponding wikipedia page; and (ii) framing the claim by directing it to consider a specific aspect. we find that the former can improve the generated * these authors equally contributed to this work. our evaluation incorporates automatic measures and manual labeling. specifically, we introduce an annotation task aiming to assess the plausibility of generated claims, i.e., to what degree is it plausible that a human will make it. we report results on a test set of 96 topics, demonstrating the validity of our approach to topics not seen in training or development. in addition, we manually annotate the generated claims for whether they are factual claims, or opinion based, and further aim to assess whether the former represent true facts. finally, we observe that manually labeled datasets used to fine-tune gpt-2 are not essential, and that relying on the output of a claim retrieval foot_0 engine for this fine-tuning, may suffice. in addition, we compare the generated claims to an existing large-scale collection of claims for the same topics, and conclude that the generated claims tend to be novel, and hence may augment traditional argument mining techniques in automatically providing claims for a given topic. henceforth, we denote the initial output of gpt-2 for a given prompt as generated text (gt). thus, our task is to define a process by which as many of the gts as possible will represent claims that are relevant to the provided prompt. ## related work in classical natural language generation (nlg) tasks -machine translation, summarization, and question answering -the semantic content of the output strongly depends on the input. argument generation, alongside story generation @xcite , occupies a parallel venue, where the output should satisfy stylistic and rhetorical constraints -yet no well-defined semantic goal -with much room and desire for innovation. approaches to argument generation have included traditional nlg architectures @xcite ; assembling arguments from given, smaller argumentative units @xcite @xcite ; welding the topic of the debate to appropriate predicates @xcite ; and using predefined argument templates @xcite . of particular interest is the generation of counter arguments, for which solutions include an encoderdecoder architecture @xcite , which may be augmented by a retrieval system @xcite , or alternatively offering "general purpose" rebuttal based on similarity to predefined claims @xcite . concurrent with our work, and most similar, is @xcite , who frame the aspect-controlled argument generation problem as follows -given a topic, a stance and an aspect, generate an argument with the given stance towards the topic, which discusses the given aspect. they finetune ctrl @xcite over claims from 8 controversial topics, and mostly use automatic measures to assess claim generation over the same 8 topics. by contrast, here we are interested in a less restricted setting and explore the properties of the generated claims. specifically, we fine-tune gpt-2 on claims coming from diverse sets of 71-192 topics, and evaluate claims generated for 96 novel topics. in this work, we assess the contribution of context to the quality of generated claims. in @xcite , context is defined as the path from a thesis (topic) node to a leaf (claim) node in an argument tree. in this work, however, we consider only arguments of depth 1, directly addressing the topic, and leave context of larger depth to future work. additionally, for development and evaluation we use human annotations alongside automatic measures, aiming to answer nuanced questions -is it plausible that the claims be asserted by a human? do the generated claims tend to be opinions or factual? and, when they are the latter, do they tend to be factually true? ## initial generation our first question was to examine the impact of the data used for fine-tuning gpt-2, aiming to identify an effective model that relies on publicly available data, and a presumably superior one that further relies on proprietary data of a much larger size. publicly available data. we considered rank-30k alone, and combined with ce2.3k. we fine-tuned gpt-2 for 2k steps on the former, and 4k steps on the latter. we denote the obtained models gpt-rank and gpt-rank-ce, respectively. proprietary data. we considered ln55k alone, as well as combined with all publicly available data. we fine-tuned gpt-2 for 8k steps on both. we denote the obtained models gpt-ln and gpt-all, respectively. 7 for each of the 4 models we generated a total of 175 gts, 5 conditioned on each of the 35 dev topics. note that the models are fine-tuned on datasets containing both supporting and contesting arguments, thus they may generate gts of both stances as well. the manual and automatic evaluation of these gts is described next. as seen in among the proprietary-based models, while gpt-all has an advantage in plausibility, perplexity, and prefix ranking accuracy, gpt-ln is better when considering the ratio of gts which are both plausible and stance-bearing -with 68% (119/175) such gts, compared to 62.3% (109/175) for gpt-2-all. it seems that adding more data, varied in type and style, could negatively impact the relevance and usefulness of gts. thus, we choose gpt-ln as the model to utilize for subsequent experiments. as for the publicly-based models, gpt-rank-ce has a small advantage in plausible and stancebearing gts, compared to gpt-rank. however, the performance of the latter is typically much better in the automatic measures. especially, we note the advantage in predicted quality -as expected, generated arguments from the gpt-rank model have higher quality, as both this model and the argument quality model were trained on a similar type of data. however, when adding the ce2.3k dataset to the training set, the quality of gts declines. thus, even though the differences between the two models are overall not substantial, we choose gpt-rank for subsequent experiments. it should be noted that there is a clear difference between the gts of gpt-ln and gpt-rank, as evident in we note that for all models, the predicted quality and stance strength are only slightly lower than their counterpart measures on the training set, suggesting that generation tends to maintain these values. ## adding context can we improve gts by conditioning their generation on more context? to evaluate this hypothesis we considered two context variations, one in which we frame the topic and the other in which we frame the claim. framing the topic. we prepend to the topic the first sentence from the wikipedia page describing the topic, to explore whether this added knowledge could guide models to generate more relevant and meaningful gts. the motivation for selecting the first sentence from wikipedia is to provide the model a concise guidance towards the respective topic via the main terms it may relate to, which usually appear in the first wikipedia sentence. the relevant wikipedia page is found by wikifying the topic, as described in §3.1. framing the claim. we also tried to append to the topic a short sentence describing an aspect relevant to discussing it, hypothesizing that adding a concrete aspect will guide the generation process in that direction. unfortunately, this did not work well, and details are deferred to the appendix. evaluation: we fine-tune gpt-2 from scratch on the modified training data of rank-30k and ln55k and refer to the new models as gpt-rank-fws, gpt-ln-fws (first wikipedia sentence, when framing the topic). we generate a sample of 5 (gpt-rank-fws) or 10 (gpt-ln-fws) gts per dev topic. ## factual, opinion, and generic claims an interesting facet when considering argumentative claims, is whether they attempt to convey facts, or rather personal opinions. thus, we explored if gts generated by our two models are characterized as more factual or opinionated. further, when considering labels with a majority vote of at least 70%, 70 of the gts generated by gpt-rank are considered factual and 63 opinion, as opposed to 46 and 105 of those generated by gpt-ln-fws, respectively. a possible explanation is that rank-30k claims -on which gpt-rank was fine-tunedtend to be more elaborate and explanatory, describing a cause and effect that correspondingly yields more factual gts; e.g., the gt genetic engineering can help further scientific developments in cancer treatment, as well as improve the long term prognosis of such diseases as help maintain a safe and effective regulatory regime for their development, for the topic we should further exploit genetic engineering. by contrast, ln55k claims are often short and concise, and perhaps more prone to express the journalist opinion; hence, training on these data yields more opinionated gts, e.g., the "sex" revolution has failed or the gender pay gap is unfair. indeed, the average number of tokens in factual gts is 17.3, compared to 14.2 for opinion gts. next, we aimed to assess whether factual gts are indeed true. a random sample of 23 and 40 factual gts generated by gpt-ln-fws and gpt-rank, respectively, were labeled for their truth value by a professional debater experienced in this task, that also was asked to assess whether the "fake facts" were nonetheless common in contemporary discourse. of the 23 gpt-ln-fws gts, 13 were considered true, the others being a mix of false or nonfactual gts. the true gts include some simple, almost trivial statements such as speed limits are designed to help reduce road fatalities, or more evidence-based facts such as rat poisons have been linked to the development of parkinson's disease, alzheimer's disease and migraines. among the 4 false gts, it is interesting, albeit perhaps unsurprising, to find that 2 were marked as common in discourse: flu vaccinations are associated with higher rates of adverse drug reactions and serious health complications, and poly-amorous relationships are linked to higher levels of sexual risk. for the 40 gpt-rank factual gts, 21 were deemed true. overall, the ratio of true gts is similar to that of gpt-ln-fws gts. it seems that some of the other gts are mixed, characterized by opening with an opinionated statement, which is followed by a factual claim, e.g., we should not abandon chain stores (opinion) as they provide a steady supply of goods and services to the community (true fact). one of the 3 false gts could be considered common in discourse, the alternative vote would cause voters to be disenfranchised. the aforementioned short gts suggested that gts tend to be rather generic, in the sense that stating that something "has failed" or "is unfair", can be done (coherently) for a great variety of contexts. indeed, such gts are reminiscent of those generated by @xcite . to assess to what extent such gts are generic, we sampled 100 of them, and annotated them ourselves. in this sample, 54 of the gts were deemed generic, suggesting that such gts are prevalent, but by no means the only types of texts being generated. we considered three existing models for ranking gts -the argument quality and stance models described in §3.3, and a claim detection (cd) proprietary service, obtained by training a bert model on ln55k. the data for training the model is augmented with negative samples from the same corpus -sub-sentential fragments which were labeled as non-claims. the objective of the model is to differentiate between claims and non-claims, and is similar to that described in @xcite for evidence detection. for evaluation we considered gts generated on the dev set by gpt-rank and gpt-ln-fws for which we had a definite label for relevance to the topic. specifically, gts which were annotated as "implausible" by a majority of annotators were assigned a label of 0. gts which were annotated as plausible, and then annotated for stance, were labeled according to the latter annotation: 1 if they were annotated as pro or con, and 0 otherwise. in total, we considered 211 positive and 120 negative gts. overall, the cd score is best correlated with the labels -pearson's ρ = 0.41, compared to 0.12 for (absolute) stance, and 0.01 for argument quality. in addition, we ranked the gts within each topic w.r.t each score, and calculated the ratio between the number of positives in the top 3 and bottom 3. as before, cd is preferred, with 81/40 positives in the top/bottom, compared to 70/56 (stance) and 71/67 (argument quality). see a short discussion about this result in the appendix. accordingly, we defined the generation pipeline as follows: (i) fine-tune gpt-2 to obtain gpt-rank (model-1) or gpt-ln-fws (model-2); (ii) generate with the topic as a prompt (model-1), or prepend the -automatically extracted -first sentence of the associated wikipedia article to the topic and use the resultant text as a prompt; (iii) rank the obtained gts according to their cd score. in principle, one could set a strict threshold on the cd score, and generate a large number of texts until a sufficient number pass this threshold. we plan to investigate this direction in future work. ## claim generation vs. claim retrieval given a controversial topic, claim generation and claim retrieval both aim to provide claims pertaining to it. it is therefore interesting to understand the interplay between the two tasks. specifically, thinking of claim generation as a mean to augment the output of claim retrieval, we ask whether gts tend to be novel, or a repetition of retrieved claims, and how does the quality of the two compare. in addition, we explore how claim retrieval can facilitate the training of the claim generation pipeline suggested in this work. how good are the generated claims? having matched gts to "real" claims allows us to compare not only their novelty, but also their quality. namely, for each of the 169 pairs we asked crowd annotators which of the two claims "would have been preferred by most people to discuss the topic?", using the same process as in section §3. among these pairs, in 41 cases both claims appeared to be similarly good (a 3:4 split); in 57 the gt is preferred; and in 71 the ln claim is considered better. among the 20 pairs which are highly similar, in 4 both claims are equally good, in 13 the gt is better and in 4 the ln claim is preferred. thus, at least in this small sample, when the two claims are conveying a similar message, human annotators seem to prefer the gpt-2 version over the human authored one. ## further observations what characterizes implausible gts? we considered the 51 gpt-ln-fws test-set gts which were deemed implausible. more than half seem to contradict common sense, often by connecting pairs of unrelated terms as in the titular the workweek is the best time to start a family, for the topic we should increase the workweek; or via connecting related terms in an odd manner as in lgbt adoption is a critical component of a child's life for the topic we should legalize lgbt adoption. other reasons for implausibility include weird phrasings (e.g., the housing in public housing is disastrously unaffordable) and bad grammar (e.g., that the benefits of the mrt network outweigh its costs). covid-19 debates. our pipeline relies heavily on the massive pre-training of gpt-2, that naturally included sentences pertaining -at least to some extent -to topics in our dev and test sets. it is therefore interesting to examine the gts obtained for topics which were presumably less abundant in the pre-training data. hence, while sheltering at home, we have generated 20 gts for each of the following two topics: we should subsidize the covid-19 drug development and coronavirus face masks should be mandatory using the gpt-ln-fws model. for the first topic, only 4 of the 20 gts were coherent and relevant, while many of the others talked about hiv, alluded to the opioid crisis, or were outright absurd -the use of artificial sweeteners in food should be a crime. the four "good" ones were of generic form, yet some showed an ability to extrapolate to relevant terms, without them being mentioned explicitly in the prefix. for example, in the gt the covid-19 vaccine will ## conclusions we suggest a claim-generation pipeline, based on a fine-tuned gpt-2 model augmented by framing the topic, and filtered using claim detection tools. results on a diverse set of 96 new topics demonstrate the merit of our approach. as expected, fine tuning on a larger dataset of claims leads to more accurate generation. yet, the coherency of the dataset also matters; simple merging of datasets of different flavors does not improve generation, and may even hamper it. to evaluate the generation models we examined several measures, which roughly estimate how "good" the generated text is. but since they do so from different perspectives, they are often not consistent with one another @xcite . here they were combined heuristically, but future work should explore this more rigorously. our work highlights some of the relations between claim generation, claim retrieval, and claim detection. in our pipeline, claim detection is used to weed out poorly-generated claims. further, we show that claim retrieval is a sufficient basis -alongside a powerful language model -for building a claim generation pipeline; and that claim generation may augment claim retrieval with additional novel claims. here, gpt-2 was used with a "default" setting. however, there is clearly an interesting trade-off between creativity and coherence, and balancing the two to fit an intended use case -perhaps even interactively -which we intend to explore in future research. finally, the claims generated by our pipeline display both subjective opinions and factual assertions. in the latter case, our initial analysis indicates that the generated claims of a factual nature are often, but certainly not always, factually true. thus, our work highlights a new emerging front in the rapidly expanding area of fact verification -that of distinguishing valid factual statements from non-valid ones, on top of automatically generated texts.