10 research outputs found
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Content Selection for Effective Counter-Argument Generation
The information ecosystem of social media has resulted in an abundance of opinions on political topics and current events. In order to encourage better discussions, it is important to promote high-quality responses and relegate low-quality ones.
We thus focus on automatically analyzing and generating counter-arguments in response to posts on social media with the goal of providing effective responses.
This thesis is composed of three parts. In the first part, we conduct an analysis of arguments. Specifically, we first annotate discussions from Reddit for aspects of arguments and then analyze them for their persuasive impact. Then we present approaches to identify the argumentative structure of these discussions and predict the persuasiveness of an argument. We evaluate each component independently using automatic or manual evaluations and show significant improvement in each.
In the second part, we leverage our discoveries from our analysis in the process of generating counter-arguments. We develop two approaches in the retrieve-and-edit framework, where we obtain content using methods created during our analysis of arguments, among others, and then modify the content using techniques from natural language generation. In the first approach, we develop an approach to retrieve counter-arguments by annotating a dataset for stance and building models for stance prediction. Then we use our approaches from our analysis of arguments to extract persuasive argumentative content before modifying non-content phrases for coherence. In contrast, in the second approach we create a dataset and models for modifying content -- making semantic edits to a claim to have a contrasting stance. We evaluate our approaches using intrinsic automatic evaluation of our predictive models and an overall human evaluation of our generated output.
Finally, in the third part, we discuss the semantic challenges of argumentation that we need to solve in order to make progress in the understanding of arguments. To clarify, we develop new methods for identifying two types of semantic relations -- causality and veracity. For causality, we build a distant-labeled dataset of causal relations using lexical indicators and then we leverage features from those indicators to build predictive models. For veracity, we build new models to retrieve evidence given a claim and predict whether the claim is supported by that evidence. We also develop a new dataset for veracity to illuminate the areas that need progress. We evaluate these approaches using automated and manual techniques and obtain significant improvement over strong baselines.
Finally, we apply these techniques to claims in the domain of household electricity consumption, mining claims using our methods for causal relations and then verifying their truthfulness
Reducing Model Jitter: Stable Re-training of Semantic Parsers in Production Environments
Retraining modern deep learning systems can lead to variations in model
performance even when trained using the same data and hyper-parameters by
simply using different random seeds. We call this phenomenon model jitter. This
issue is often exacerbated in production settings, where models are retrained
on noisy data. In this work we tackle the problem of stable retraining with a
focus on conversational semantic parsers. We first quantify the model jitter
problem by introducing the model agreement metric and showing the variation
with dataset noise and model sizes. We then demonstrate the effectiveness of
various jitter reduction techniques such as ensembling and distillation.
Lastly, we discuss practical trade-offs between such techniques and show that
co-distillation provides a sweet spot in terms of jitter reduction for semantic
parsing systems with only a modest increase in resource usage.Comment: SIGDIAL 2022 Best Pape
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue
Modern virtual assistants use internal semantic parsing engines to convert
user utterances to actionable commands. However, prior work has demonstrated
that semantic parsing is a difficult multilingual transfer task with low
transfer efficiency compared to other tasks. In global markets such as India
and Latin America, this is a critical issue as switching between languages is
prevalent for bilingual users. In this work we dramatically improve the
zero-shot performance of a multilingual and codeswitched semantic parsing
system using two stages of multilingual alignment. First, we show that
constrastive alignment pretraining improves both English performance and
transfer efficiency. We then introduce a constrained optimization approach for
hyperparameter-free adversarial alignment during finetuning. Our Doubly Aligned
Multilingual Parser (DAMP) improves mBERT transfer performance by 3x, 6x, and
81x on the Spanglish, Hinglish and Multilingual Task Oriented Parsing
benchmarks respectively and outperforms XLM-R and mT5-Large using 3.2x fewer
parameters.Comment: 9 Pages; ACL Main Conference 202
What changed your mind : the roles of dynamic topics and discourse in argumentation process
In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the in- creasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful -- topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations
Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies
Intelligent conversational agents, or chatbots, can take on various
identities and are increasingly engaging in more human-centered conversations
with persuasive goals. However, little is known about how identities and
inquiry strategies influence the conversation's effectiveness. We conducted an
online study involving 790 participants to be persuaded by a chatbot for
charity donation. We designed a two by four factorial experiment (two chatbot
identities and four inquiry strategies) where participants were randomly
assigned to different conditions. Findings showed that the perceived identity
of the chatbot had significant effects on the persuasion outcome (i.e.,
donation) and interpersonal perceptions (i.e., competence, confidence, warmth,
and sincerity). Further, we identified interaction effects among perceived
identities and inquiry strategies. We discuss the findings for theoretical and
practical implications for developing ethical and effective persuasive
chatbots. Our published data, codes, and analyses serve as the first step
towards building competent ethical persuasive chatbots.Comment: 15 pages, 10 figures. Full paper to appear at ACM CHI 202