Argumentation is a type of discourse where speakers try to persuade their audience about the reasonableness of a claim by presenting supportive arguments. Most work in argument mining has focused on modeling arguments in monologues. We propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macrolevel (argument as process) models of argumentation. Fundamentally, this approach relies on identifying relations between components of arguments in a discussion thread. Our approach for relation prediction uses contextual information in terms of fine-tuning a pretrained language model and leveraging discourse relations based on Rhetorical Structure Theory. We additionally propose a candidate selection method to automatically predict what parts of one's argument will be targeted by other participants in the discussion. Our models obtain significant improvements compared to recent state-of-the-art approaches using pointer networks and a pre-trained language model.

Long-form narrative text generated from large language models manages a fluent impersonation of human writing, but only at the local sentence level, and lacks structure or global cohesion. We posit that many of the problems of story generation can be addressed via highquality content planning, and present a system that focuses on how to learn good plot structures to guide story generation. We utilize a plot-generation language model along with an ensemble of rescoring models that each implement an aspect of good story-writing as detailed in Aristotle's Poetics. We find that stories written with our more principled plotstructure are both more relevant to a given prompt and higher quality than baselines that do not content plan, or that plan in an unprincipled way. 1 Prompt: Write the most beautiful end to the universe that you can imagine Naive Plot with Semantic Role Labelling Tags: The universe end ent 0 see ent 3 dying ent 1 # ent 1 left ent 0 the last human live ent 6 end # ent 2 come the last one die a universe of life left Revised Plot with Semantic Role Labelling Tags: The light filled the sky A bright flash lit # began to grow # the stars grow began ent 0 to fade # The stars fade ent 2 looked ent 1 me dying star Naive Generated Story: "You're dead. I'll be dead, and there'll always be no more of you."* *"No, my dear. It's not true!" -The last of the world is dead, * * "It'll never be over. The universe will n't end. Not yet. But I'll see it, one way or another. There is no other way."* *My last thought, the dying man left me. He told that I was the last human to live. And he told me the universe would end, and the end will come soon. That your death would be the last one to die, and the first one to be the most beautiful. * "There is a universe of life left, my dear, and that's all there is. Generated Story (from revision): It was the end of the universe. The light filled the sky. The galaxy. All the light in all the worlds, and all the stars. A bright flash. The first light. And then a bright flash of light... then a flash of darkness. Then a bright light. A flash of blackness and then the stars began to grow. It was a bright, beautiful bright, light. And then, slowly the stars started to grow in number. There were millions and millions of stars.. But, slowly but surely, all the galaxies began to fade. Slowly, but surely. Finally, it looked at me with its eyes. One dying star. Gold Story: It slept, and dreamt of twirling lights. It cradled countless twirling lights, all dancing in their flashing ways, dancing to the sound of a faraway flute. The light was vibrant and warm and spun all around It. The tiny lights grew with the tune, and more lights came to dance within the luminescence. It was surrounded by light, all waltzing in their ways t...

We introduce a FEVER-like dataset COVID-Fact of 4, 086 claims concerning the COVID-19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence. Unlike previous approaches, we automatically detect true claims and their source articles and then generate counter-claims using automatic methods rather than employing human annotators. Along with our constructed resource, we formally present the task of identifying relevant evidence for the claims and verifying whether the evidence refutes or supports a given claim. In addition to scientific claims, our data contains simplified general claims from media sources, making it better suited for detecting general misinformation regarding COVID-19. Our experiments indicate that COVID-Fact will provide a challenging testbed for the development of new systems and our approach will reduce the costs of building domainspecific datasets for detecting misinformation. Original ClaimClosed environments facilitate secondary transmission of coronavirus disease 2019 Counter-Claim Closed environments prevent secondary transmission of coronavirus disease 2019 Gold Document

Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language, such as a simile, goes beyond plain expressions to give readers new insights and inspirations. We tackle the problem of simile generation. Generating a simile requires proper understanding for effective mapping of properties between two concepts. To this end, we first propose a method to automatically construct a parallel corpus by transforming a large number of similes collected from Reddit to their literal counterpart using structured common sense knowledge. We then fine-tune a pretrained sequence to sequence model, BART (Lewis et al., 2019), on the literal-simile pairs to generate novel similes given a literal sentence. Experiments show that our approach generates 88% novel similes that do not share properties with the training data. Human evaluation on an independent set of literal statements shows that our model generates similes better than two literary experts 37% 1 of the times, and three baseline systems including a recent metaphor generation model 71% 2 of the times when compared pairwise. 3 We also show how replacing literal sentences with similes from our best model in machine generated stories improves evocativeness and leads to better acceptance by human judges. * The research was conducted when the author was at USC/ISI. 1 We average 32.6% and 41.3% for 2 humans. 2 We average 82% ,63% and 68% for three baselines. 3 The simile in the title is generated by our best model. Input: Generating similes effortlessly, output: Generating similes like a Pro.

We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-andedit framework to instantiate two major characteristics of sarcasm: reversal of valence and semantic incongruity with the context, which could include shared commonsense or world knowledge between the speaker and the listener. While prior works on sarcasm generation predominantly focus on context incongruity, we show that combining valence reversal and semantic incongruity based on commonsense knowledge generates sarcastic messages of higher quality based on several criteria. Human evaluation shows that our system generates sarcasm better than human judges 34% of the time, and better than a reinforced hybrid baseline 90% of the time.

This paper presents the ColumbiaNLP submission for the FEVER Workshop Shared Task. Our system is an end-to-end pipeline that extracts factual evidence from Wikipedia and infers a decision about the truthfulness of the claim based on the extracted evidence. Our pipeline achieves significant improvement over the baseline for all the components (Document Retrieval, Sentence Selection and Textual Entailment) both on the development set and the test set. Our team finished 6th out of 24 teams on the leader-board based on the preliminary results with a FEVER score of 49.06 on the blind test set compared to 27.45 of the baseline system.

Generating metaphors is a challenging task as it requires a proper understanding of abstract concepts, making connections between unrelated concepts, and deviating from the literal meaning. In this paper, we aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. Based on a theoretically-grounded connection between metaphors and symbols, we propose a method to automatically construct a parallel corpus by transforming a large number of metaphorical sentences from the Gutenberg Poetry corpus (Jacobs, 2018) to their literal counterpart using recent advances in masked language modeling coupled with commonsense inference. For the generation task, we incorporate a metaphor discriminator to guide the decoding of a sequence to sequence model finetuned on our parallel data to generate high quality metaphors. Human evaluation on an independent test set of literal statements shows that our best model generates metaphors better than three well-crafted baselines 66% of the time on average. Moreover, a task-based evaluation shows that human-written poems enhanced with metaphors proposed by our model are preferred 68% of the time compared to poems without metaphors.

The goal of any social media platform is to facilitate healthy and meaningful interactions among its users. But more often than not, it has been found that it becomes an avenue for wanton attacks. We propose an experimental study that has three aims: 1) to provide us with a deeper understanding of current datasets that focus on different types of abusive language, which are sometimes overlapping (racism, sexism, hate speech, offensive language and personal attacks); 2) to investigate what type of attention mechanism (contextual vs. self-attention) is better for abusive language detection using deep learning architectures; and 3) to investigate whether stacked architectures provide an advantage over simple architectures for this task.