33 research outputs found
DisCGen: A Framework for Discourse-Informed Counterspeech Generation
Counterspeech can be an effective method for battling hateful content on
social media. Automated counterspeech generation can aid in this process.
Generated counterspeech, however, can be viable only when grounded in the
context of topic, audience and sensitivity as these factors influence both the
efficacy and appropriateness. In this work, we propose a novel framework based
on theories of discourse to study the inferential links that connect counter
speeches to the hateful comment. Within this framework, we propose: i) a
taxonomy of counterspeech derived from discourse frameworks, and ii)
discourse-informed prompting strategies for generating contextually-grounded
counterspeech. To construct and validate this framework, we present a process
for collecting an in-the-wild dataset of counterspeech from Reddit. Using this
process, we manually annotate a dataset of 3.9k Reddit comment pairs for the
presence of hatespeech and counterspeech. The positive pairs are annotated for
10 classes in our proposed taxonomy. We annotate these pairs with paraphrased
counterparts to remove offensiveness and first-person references. We show that
by using our dataset and framework, large language models can generate
contextually-grounded counterspeech informed by theories of discourse.
According to our human evaluation, our approaches can act as a safeguard
against critical failures of discourse-agnostic models.Comment: IJCNLP-AACL, 202
Learning to Generate Equitable Text in Dialogue from Biased Training Data
The ingrained principles of fairness in a dialogue system's decision-making
process and generated responses are crucial for user engagement, satisfaction,
and task achievement. Absence of equitable and inclusive principles can hinder
the formation of common ground, which in turn negatively impacts the overall
performance of the system. For example, misusing pronouns in a user interaction
may cause ambiguity about the intended subject. Yet, there is no comprehensive
study of equitable text generation in dialogue. Aptly, in this work, we use
theories of computational learning to study this problem. We provide formal
definitions of equity in text generation, and further, prove formal connections
between learning human-likeness and learning equity: algorithms for improving
equity ultimately reduce to algorithms for improving human-likeness (on
augmented data). With this insight, we also formulate reasonable conditions
under which text generation algorithms can learn to generate equitable text
without any modifications to the biased training data on which they learn. To
exemplify our theory in practice, we look at a group of algorithms for the
GuessWhat?! visual dialogue game and, using this example, test our theory
empirically. Our theory accurately predicts relative-performance of multiple
algorithms in generating equitable text as measured by both human and automated
evaluation
Clue: Cross-modal Coherence Modeling for Caption Generation
We use coherence relations inspired by computational models of discourse to
study the information needs and goals of image captioning. Using an annotation
protocol specifically devised for capturing image--caption coherence relations,
we annotate 10,000 instances from publicly-available image--caption pairs. We
introduce a new task for learning inferences in imagery and text, coherence
relation prediction, and show that these coherence annotations can be exploited
to learn relation classifiers as an intermediary step, and also train
coherence-aware, controllable image captioning models. The results show a
dramatic improvement in the consistency and quality of the generated captions
with respect to information needs specified via coherence relations.Comment: Accepted as a long paper to ACL 202
APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations
Using style-transfer models to reduce offensiveness of social media comments
can help foster a more inclusive environment. However, there are no sizable
datasets that contain offensive texts and their inoffensive counterparts, and
fine-tuning pretrained models with limited labeled data can lead to the loss of
original meaning in the style-transferred text. To address this issue, we
provide two major contributions. First, we release the first
publicly-available, parallel corpus of offensive Reddit comments and their
style-transferred counterparts annotated by expert sociolinguists. Then, we
introduce the first discourse-aware style-transfer models that can effectively
reduce offensiveness in Reddit text while preserving the meaning of the
original text. These models are the first to examine inferential links between
the comment and the text it is replying to when transferring the style of
offensive Reddit text. We propose two different methods of integrating
discourse relations with pretrained transformer models and evaluate them on our
dataset of offensive comments from Reddit and their inoffensive counterparts.
Improvements over the baseline with respect to both automatic metrics and human
evaluation indicate that our discourse-aware models are better at preserving
meaning in style-transferred text when compared to the state-of-the-art
discourse-agnostic models.Comment: To be published in Proceedings of COLING 2022, the 29th International
Conference on Computational Linguistic
Modeling Non-Cooperative Dialogue: Theoretical and Empirical Insights
Investigating cooperativity of interlocutors is central in studying
pragmatics of dialogue. Models of conversation that only assume cooperative
agents fail to explain the dynamics of strategic conversations. Thus, we
investigate the ability of agents to identify non-cooperative interlocutors
while completing a concurrent visual-dialogue task. Within this novel setting,
we study the optimality of communication strategies for achieving this
multi-task objective. We use the tools of learning theory to develop a
theoretical model for identifying non-cooperative interlocutors and apply this
theory to analyze different communication strategies. We also introduce a
corpus of non-cooperative conversations about images in the GuessWhat?! dataset
proposed by De Vries et al. (2017). We use reinforcement learning to implement
multiple communication strategies in this context and find empirical results
validate our theory
Intention and Attention in Image-Text Presentations: A Coherence Approach
In image-text presentations from online discourse, pronouns can refer to entities depicted in images, even if these entities are not otherwise referred to in a text caption. While visual salience may be enough to allow a writer to use a pronoun to refer to a prominent entity in the image, coherence theory suggests that pronoun use is more restricted. Specifically, language users may need an appropriate coherence relation between text and imagery to license and resolve pronouns. To explore this hypothesis and better understand the relationship between image context and text interpretation, we annotated an image-text data set with coherence relations and pronoun information. We find that pronoun use reflects a complex interaction between the content of the pronoun, the grammar of the text, and the relation of text and image
PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners
Multiclass neural networks are a common tool in modern unsupervised domain
adaptation, yet an appropriate theoretical description for their non-uniform
sample complexity is lacking in the adaptation literature. To fill this gap, we
propose the first PAC-Bayesian adaptation bounds for multiclass learners. We
facilitate practical use of our bounds by also proposing the first
approximation techniques for the multiclass distribution divergences we
consider. For divergences dependent on a Gibbs predictor, we propose additional
PAC-Bayesian adaptation bounds which remove the need for inefficient
Monte-Carlo estimation. Empirically, we test the efficacy of our proposed
approximation techniques as well as some novel design-concepts which we include
in our bounds. Finally, we apply our bounds to analyze a common adaptation
algorithm that uses neural networks
NAREOR: The Narrative Reordering Problem
Many implicit inferences exist in text depending on how it is structured that
can critically impact the text's interpretation and meaning. One such
structural aspect present in text with chronology is the order of its
presentation. For narratives or stories, this is known as the narrative order.
Reordering a narrative can impact the temporal, causal, event-based, and other
inferences readers draw from it, which in turn can have strong effects both on
its interpretation and interestingness. In this paper, we propose and
investigate the task of Narrative Reordering (NAREOR) which involves rewriting
a given story in a different narrative order while preserving its plot. We
present a dataset, NAREORC, with human rewritings of stories within ROCStories
in non-linear orders, and conduct a detailed analysis of it. Further, we
propose novel task-specific training methods with suitable evaluation metrics.
We perform experiments on NAREORC using state-of-the-art models such as BART
and T5 and conduct extensive automatic and human evaluations. We demonstrate
that although our models can perform decently, NAREOR is a challenging task
with potential for further exploration. We also investigate two applications of
NAREOR: generation of more interesting variations of stories and serving as
adversarial sets for temporal/event-related tasks, besides discussing other
prospective ones, such as for pedagogical setups related to language skills
like essay writing and applications to medicine involving clinical narratives.Comment: Accepted to AAAI 202
That and There: Judging the Intent of Pointing Actions with Robotic Arms
Collaborative robotics requires effective communication between a robot and a
human partner. This work proposes a set of interpretive principles for how a
robotic arm can use pointing actions to communicate task information to people
by extending existing models from the related literature. These principles are
evaluated through studies where English-speaking human subjects view animations
of simulated robots instructing pick-and-place tasks. The evaluation
distinguishes two classes of pointing actions that arise in pick-and-place
tasks: referential pointing (identifying objects) and locating pointing
(identifying locations). The study indicates that human subjects show greater
flexibility in interpreting the intent of referential pointing compared to
locating pointing, which needs to be more deliberate. The results also
demonstrate the effects of variation in the environment and task context on the
interpretation of pointing. Our corpus, experiments and design principles
advance models of context, common sense reasoning and communication in embodied
communication.Comment: Accepted to AAAI 2020, New York Cit