6 research outputs found
Identifying attack and support argumentative relations using deep learning
We propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to an- other, of the kind that naturally occur in a debate. The architecture uses two (uni- directional or bidirectional) Long Short- Term Memory networks and (trained or non-trained) word embeddings, and al- lows to considerably improve upon exist- ing techniques that use syntactic features and supervised classifiers for the same form of (relation-based) argument mining
Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning
Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal
A Graph-Based Context-Aware Model to Understand Online Conversations
Online forums that allow for participatory engagement between users have been
transformative for the public discussion of many important issues. However,
such conversations can sometimes escalate into full-blown exchanges of hate and
misinformation. Existing approaches in natural language processing (NLP), such
as deep learning models for classification tasks, use as inputs only a single
comment or a pair of comments depending upon whether the task concerns the
inference of properties of the individual comments or the replies between pairs
of comments, respectively. But in online conversations, comments and replies
may be based on external context beyond the immediately relevant information
that is input to the model. Therefore, being aware of the conversations'
surrounding contexts should improve the model's performance for the inference
task at hand.
We propose GraphNLI, a novel graph-based deep learning architecture that uses
graph walks to incorporate the wider context of a conversation in a principled
manner. Specifically, a graph walk starts from a given comment and samples
"nearby" comments in the same or parallel conversation threads, which results
in additional embeddings that are aggregated together with the initial
comment's embedding. We then use these enriched embeddings for downstream NLP
prediction tasks that are important for online conversations. We evaluate
GraphNLI on two such tasks - polarity prediction and misogynistic hate speech
detection - and found that our model consistently outperforms all relevant
baselines for both tasks. Specifically, GraphNLI with a biased root-seeking
random walk performs with a macro-F1 score of 3 and 6 percentage points better
than the best-performing BERT-based baselines for the polarity prediction and
hate speech detection tasks, respectively.Comment: 25 pages, 9 figures. arXiv admin note: text overlap with
arXiv:2202.0817
Who has the last word? Understanding How to Sample Online Discussions
In online debates individual arguments support or attack each other, leading
to some subset of arguments being considered more relevant than others.
However, in large discussions readers are often forced to sample a subset of
the arguments being put forth. Since such sampling is rarely done in a
principled manner, users may not read all the relevant arguments to get a full
picture of the debate. This paper is interested in answering the question of
how users should sample online conversations to selectively favour the
currently justified or accepted positions in the debate. We apply techniques
from argumentation theory and complex networks to build a model that predicts
the probabilities of the normatively justified arguments given their location
in online discussions. Our model shows that the proportion of replies that are
supportive, the number of replies that comments receive, and the locations of
un-replied comments all determine the probability that a comment is a justified
argument. We show that when the degree distribution of the number of replies is
homogeneous along the discussion, for acrimonious discussions, the distribution
of justified arguments depends on the parity of the graph level. In supportive
discussions the probability of having justified comments increases as one moves
away from the root. For discussion trees that have a non-homogeneous in-degree
distribution, for supportive discussions we observe the same behaviour as
before, while for acrimonious discussions we cannot observe the same
parity-based distribution. This is verified with data obtained from the online
debating platform Kialo. By predicting the locations of the justified arguments
in reply trees, we can suggest which arguments readers should sample to grasp
the currently accepted opinions in such discussions. Our models have important
implications for the design of future online debating platforms
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Building a corpus of legal argumentation in Japanese judgement documents: towards structure-based summarisation
Argumentation dialogues in web-based GDSS: an approach using machine learning techniques
Tese de doutoramento em InformaticsA tomada de decisão está presente no dia a dia de qualquer pessoa, mesmo que muitas vezes ela
não tenha consciência disso. As decisões podem estar relacionadas com problemas quotidianos, ou
podem estar relacionadas com questões mais complexas, como é o caso das questões organizacionais.
Normalmente, no contexto organizacional, as decisões são tomadas em grupo.
Os Sistemas de Apoio à Decisão em Grupo têm sido estudados ao longo das últimas décadas com o
objetivo de melhorar o apoio prestado aos decisores nas mais diversas situações e/ou problemas a resolver.
Existem duas abordagens principais à implementação de Sistemas de Apoio à Decisão em Grupo:
a abordagem clássica, baseada na agregação matemática das preferências dos diferentes elementos do
grupo e as abordagens baseadas na negociação automática (e.g. Teoria dos Jogos, Argumentação, entre
outras).
Os atuais Sistemas de Apoio à Decisão em Grupo baseados em argumentação podem gerar uma
enorme quantidade de dados. O objetivo deste trabalho de investigação é estudar e desenvolver modelos
utilizando técnicas de aprendizagem automática para extrair conhecimento dos diálogos argumentativos
realizados pelos decisores, mais concretamente, pretende-se criar modelos para analisar, classificar e
processar esses dados, potencializando a geração de novo conhecimento que será utilizado tanto por
agentes inteligentes, como por decisiores reais. Promovendo desta forma a obtenção de consenso entre
os membros do grupo. Com base no estudo da literatura e nos desafios em aberto neste domínio,
formulou-se a seguinte hipótese de investigação - É possível usar técnicas de aprendizagem automática
para apoiar diálogos argumentativos em Sistemas de Apoio à Decisão em Grupo baseados na web.
No âmbito dos trabalhos desenvolvidos, foram aplicados algoritmos de classificação supervisionados
a um conjunto de dados contendo argumentos extraídos de debates online, criando um classificador
de frases argumentativas que pode classificar automaticamente (A favor/Contra) frases argumentativas
trocadas no contexto da tomada de decisão. Foi desenvolvido um modelo de clustering dinâmico para
organizar as conversas com base nos argumentos utilizados. Além disso, foi proposto um Sistema de
Apoio à Decisão em Grupo baseado na web que possibilita apoiar grupos de decisores independentemente
de sua localização geográfica. O sistema permite a criação de problemas multicritério e a configuração
das preferências, intenções e interesses de cada decisor. Este sistema de apoio à decisão baseado na
web inclui os dashboards de relatórios inteligentes que são gerados através dos resultados dos trabalhos
alcançados pelos modelos anteriores já referidos. A concretização de cada um dos objetivos permitiu
validar as questões de investigação identificadas e assim responder positivamente à hipótese definida.Decision-making is present in anyone’s daily life, even if they are often unaware of it. Decisions can be
related to everyday problems, or they can be related to more complex issues, such as organizational
issues. Normally, in the organizational context, decisions are made in groups.
Group Decision Support Systems have been studied over the past decades with the aim of improving
the support provided to decision-makers in the most diverse situations and/or problems to be solved.
There are two main approaches to implementing Group Decision Support Systems: the classical approach,
based on the mathematical aggregation of the preferences of the different elements of the group, and the
approaches based on automatic negotiation (e.g. Game Theory, Argumentation, among others).
Current argumentation-based Group Decision Support Systems can generate an enormous amount
of data. The objective of this research work is to study and develop models using automatic learning techniques
to extract knowledge from argumentative dialogues carried out by decision-makers, more specifically,
it is intended to create models to analyze, classify and process these data, enhancing the generation
of new knowledge that will be used both by intelligent agents and by real decision-makers. Promoting in
this way the achievement of consensus among the members of the group. Based on the literature study
and the open challenges in this domain, the following research hypothesis was formulated - It is possible
to use machine learning techniques to support argumentative dialogues in web-based Group Decision
Support Systems.
As part of the work developed, supervised classification algorithms were applied to a data set containing
arguments extracted from online debates, creating an argumentative sentence classifier that can
automatically classify (For/Against) argumentative sentences exchanged in the context of decision-making.
A dynamic clustering model was developed to organize conversations based on the arguments used. In
addition, a web-based Group Decision Support System was proposed that makes it possible to support
groups of decision-makers regardless of their geographic location. The system allows the creation of multicriteria
problems and the configuration of preferences, intentions, and interests of each decision-maker.
This web-based decision support system includes dashboards of intelligent reports that are generated
through the results of the work achieved by the previous models already mentioned. The achievement of
each objective allowed validation of the identified research questions and thus responded positively to the
defined hypothesis.I also thank to Fundação para a Ciência e a Tecnologia, for the Ph.D. grant funding with the reference: SFRH/BD/137150/2018