Argument maps structure discourse into nodes in a tree with each node being an argument that supports or opposes its parent argument. This format is more comprehensible and less redundant compared to an unstructured one. Exploring those maps and maintaining their structure by placing new arguments under suitable parents is more challenging for users with huge maps that are typical in online discussions. To support those users, we introduce the task of node placement: suggesting candidate nodes as parents for a new contribution. We establish an upper-bound of human performance, and conduct experiments with models of various sizes and training strategies. We experiment with a selection of maps from Kialo, drawn from a heterogeneous set of domains. Based on an annotation study, we highlight the ambiguity of the task that makes it challenging for both humans and models. We examine the unidirectional relation between tree nodes and show that encoding a node into different embeddings for each of the parent and child cases improves performance. We further show the few-shot effectiveness of our approach.

<iframe src="https://app.sli.do/event/aL2QM8dUtT8fzBuK3envdP/embed/polls/d53fc125-db4c-4b3e-9644-3c82095a1dff" width="300" height="400"></iframe>

Node Placement in Argument Maps: Modeling Unidirectional Relations in High & Low-Resource Scenarios

Translate-train or few-shot cross-lingual transfer can be used to improve the zero-shot performance of multilingual pretrained language models.  Few-shot utilizes high-quality low-quantity samples (often manually translated from the English corpus ). Translate-train employs a machine translation of the English corpus, resulting in samples with lower quality that could be scaled to high quantity. Given the lower cost and higher availability of machine translation compared to manual professional translation, it is important to systematically compare few-shot and translate-train, understand when each has an advantage,  and investigate how to choose the shots to translate in order to increase the few-shot gain.  This work aims to fill this gap: we compare and quantify the performance gain of few-shot vs. translate-train using three different base models and a varying number of samples for three tasks/datasets (XNLI, PAWS-X, XQuAD) spanning 17 languages. We show that scaling up the training data using machine translation gives a larger gain compared to using the small-scale (higher-quality) few-shot data. When few-shot is beneficial, we show that there are random sets of samples that perform better across languages and that the performance on English and on the machine-translation of the samples can both be used to choose the shots to manually translate for an increased few-shot gain.

How to Translate Your Samples and Choose Your Shots? Analyzing Translate-train & Few-shot Cross-lingual Transfer

This survey builds an interdisciplinary picture of Argument Mining (AM), with a strong focus on its potential to address issues related to Social and Political Science. More specifically, we focus on AM challenges related to its applications to social media and in the multilingual domain, and then proceed to the widely debated notion of argument quality. We propose a novel definition of argument quality which is integrated with that of deliberative quality from the Social Science literature. Under our definition, the quality of a contribution needs to be assessed at multiple levels: the contribution itself, its preceding context, and the consequential effect on the development of the upcoming discourse. The latter has not received the deserved attention within the community. We finally define an application of AM for Social Good: (semi-)automatic moderation, a highly integrative application which (a) represents a challenging testbed for the integrated notion of quality we advocate, (b) allows the empirical quantification of argument/deliberative quality to benefit from the developments in other NLP fields (i.e. hate speech detection, fact checking, debiasing), and (c) has a clearly beneficial potential at the level of its societal thanks to its real-world application (even if extremely ambitious).

Towards Argument Mining for Social Good: A Survey

Sentiment Analysis, Stylistic Analysis, and Argument Mining

technical paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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## Welcome to NAACL 2022!
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Poster 1O: Theme

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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Node Placement in Argument Maps: Modeling Unidirectional Relations in High & Low-Resource Scenarios

How to Translate Your Samples and Choose Your Shots? Analyzing Translate-train & Few-shot Cross-lingual Transfer

Towards Argument Mining for Social Good: A Survey

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