Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models (2021)

Abstract: Enthymemes are defined as arguments where a premise or conclusion is left implicit. We tackle the task of generating the implicit premise in an enthymeme , which requires not only an understanding of the stated conclusion and premise but also additional inferences that could depend on commonsense knowledge. The largest available dataset for enthymemes (Habernal et al., 2018) consists of 1.7k samples, which is not large enough to train a neural text generation model. To address this issue, we take advantage of a similar task and dataset: Abductive reasoning in narrative text (Bhagavatula et al., 2020). However, we show that simply using a state-of-the-art seq2seq model fine-tuned on this data might not generate meaningful implicit premises associated with the given enthymemes. We demonstrate that encoding discourse-aware commonsense during fine-tuning improves the quality of the generated implicit premises and outperforms all other baselines both in automatic and human evaluations on three different datasets.

## introduction in argumentation theory, an enthymeme is defined as an incomplete argument found in discourse, where some components are explicit, but other propositions are left implicit and need to be filled in as premises or conclusions to fully understand what the argument is @xcite . in many instances the missing proposition is a premise. the well-cited example of the silver blade case from one of sherlock holmes' stories @xcite presents such as an incomplete argument a dog was kept in the stable, and yet, though someone had been in and fetched out a horse, he had not barked enough to rouse the two lads in the loft. obviously, the midnight visitor was someone whom the dog knew well. ## related work prior work on enthymeme reconstruction has focused primarily on the identification (i.e., classification) of implicit premises in enthymemes @xcite @xcite @xcite . @xcite study how to identify enthymemes in online discussions, while @xcite present the task of identifying the correct warrant given two candidates warrants in order to reconstruct an enthymeme. @xcite introduce an approach to classify the stance of a statement as implicit or explicit, as a first step towards the long term goal of enthymeme reconstruction. unlike these works which propose discriminative approaches to identify an enthymeme or the (correct) implicit premises, we focus on generative o1 alex had his heart set on an ivy league college o2 alex ended up achieving his dream of getting into the school. h alex applied to harvard encoder input amy was looking through her mother's old scrapbooks. [sep] amy realized her mother had dated her history professor. encoder input + para-comet amy was looking through her mother's old scrapbooks. [sep] to find something [sep] amy realized her mother had dated her history professor. decoder ouput amy was looking through her mother's old scrapbooks. and since amy found pictures of her history professor and mother together. amy realized her mother had dated her history professor. test datasets. we test our models on three different datasets of incomplete arguments (enthymeme) annotated with human-generated implicit/missing premises. first, we use the argument reasoning comprehension task dataset released by @xcite (d1), which contains 1654 {claim, premise, warrant(implicit premise)} triples. second, we used the dataset introduced by @xcite , which contains 494 enthymemes from an online debate forum with human annotated implicit premises (d2). third, we use the dataset introduced by becker et al. (2020) (d3), which contains implicit premises annotated for each arguments from the microtext corpus @xcite . for d3, we focus only arguments that are in a support relation since this corresponds to our task. moreover, we choose the cases where there is only one implicit premise, rather than a chain of linked premises. this results in a total of 112 enthymemes for d3. for all datasets, we apply automatic filtering to keep only full-formed sentences as claim and premises (e.g., remove cases where the stated premise/claim consists of a nounphrase, a partial clauses, or many sentences). ## method for our generation model, we use bart @xcite , a pre-trained conditional language model that combines bidirectional and autoregressive transformers. it is implemented as a sequence-to-sequence model with a bidirectional encoder over corrupted text and a left-to-right autoregressive decoder. ## evaluation and results we evaluate three setups: 1) directly use pre-trained bart (zero-shot); 2) fine-tune bart on art; 3) fine-tune bart on art+para-comet. ## conclusions we propose an end-to-end approach for a new task of automatically generating an implicit premise given an enthymeme. we show how leveraging abductive reasoning as an auxiliary task improves over zero-shot performance of a state-of-the-art generative language model. finally, we build a knowledge-enhanced model by encoding discourseaware commonsense that outperforms all existing baselines in terms of automatic metrics as well as plausibility judgements from crowdworkers. future work includes exploring other sources for commonsense knowledge, experimenting with improved decoding techniques, as well as studying the role of argument schemes in enthymemes reconstruction.