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Found 35 papers, 14 papers with code
Multi-modal Intent Classification for Assistive Robots with Large-scale Naturalistic Datasets
These technologies aim to help the users with various reaching and grasping tasks in their daily lives such as picking up an object and transporting it to a desired location; and their utility critically depends on the ease and effectiveness of communication between the user and robot.
Principled Analysis of Energy Discourse across Domains with Thesaurus-based Automatic Topic Labeling
With the increasing impact of Natural Language Processing tools like topic models in social science research, the experimental rigor and comparability of models and datasets has come under scrutiny.
Connecting the Dots in News Analysis: A Cross-Disciplinary Survey of Media Bias and Framing
The manifestation and effect of bias in news reporting have been central topics in the social sciences for decades, and have received increasing attention in the NLP community recently.
Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing
Despite increasing interest in the automatic detection of media frames in NLP, the problem is typically simplified as single-label classification and adopts a topic-like view on frames, evading modelling the broader document-level narrative.
A Large-Scale Multilingual Study of Visual Constraints on Linguistic Selection of Descriptions
We study the relation between visual input and linguistic choices by training classifiers to predict the probability of expressing a property from raw images, and find evidence supporting the claim that linguistic properties are constrained by visual context across languages.
Professional Presentation and Projected Power: A Case Study of Implicit Gender Information in English CVs
Gender discrimination in hiring is a pertinent and persistent bias in society, and a common motivating example for exploring bias in NLP.
Systematic Evaluation of Predictive Fairness
Mitigating bias in training on biased datasets is an important open problem.
A Computational Acquisition Model for Multimodal Word Categorization
Recent advances in self-supervised modeling of text and images open new opportunities for computational models of child language acquisition, which is believed to rely heavily on cross-modal signals.
Optimising Equal Opportunity Fairness in Model Training
Real-world datasets often encode stereotypes and societal biases.
fairlib: A Unified Framework for Assessing and Improving Classification Fairness
This paper presents fairlib, an open-source framework for assessing and improving classification fairness.
Unsupervised Cross-Lingual Transfer of Structured Predictors without Source Data
Providing technologies to communities or domains where training data is scarce or protected e. g., for privacy reasons, is becoming increasingly important.
Contrastive Learning for Fair Representations
Trained classification models can unintentionally lead to biased representations and predictions, which can reinforce societal preconceptions and stereotypes.
Fairness-aware Class Imbalanced Learning
Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups.
Evaluating Debiasing Techniques for Intersectional Biases
Bias is pervasive in NLP models, motivating the development of automatic debiasing techniques.
Commonsense Knowledge in Word Associations and ConceptNet
Humans use countless basic, shared facts about the world to efficiently navigate in their environment.
PTST-UoM at SemEval-2021 Task 10: Parsimonious Transfer for Sequence Tagging
This paper describes PTST, a source-free unsupervised domain adaptation technique for sequence tagging, and its application to the SemEval-2021 Task 10 on time expression recognition.
Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames
Understanding how news media frame political issues is important due to its impact on public attitudes, yet hard to automate.
PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation
Cross-lingual transfer is a leading technique for parsing low-resource languages in the absence of explicit supervision.
Screenplay Summarization Using Latent Narrative Structure
Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront.
Partners in Crime: Multi-view Sequential Inference for Movie Understanding
Multi-view learning algorithms are powerful representation learning tools, often exploited in the context of multimodal problems.
Extractive NarrativeQA with Heuristic Pre-Training
On QA from full narratives, our model outperforms previous models on the METEOR metric.
Book QA: Stories of Challenges and Opportunities
We present a system for answering questions based on the full text of books (BookQA), which first selects book passages given a question at hand, and then uses a memory network to reason and predict an answer.
A Probabilistic Framework for Learning Domain Specific Hierarchical Word Embeddings
The meaning of a word often varies depending on its usage in different domains.
BookQA: Stories of Challenges and Opportunities
We present a system for answering questions based on the full text of books (BookQA), which first selects book passages given a question at hand, and then uses a memory network to reason and predict an answer.
Inducing Document Structure for Aspect-based Summarization
In addition to improvements in summarization over topic-agnostic baselines, we demonstrate the benefit of the learnt document structure: we show that our models (a) learn to accurately segment documents by aspect; (b) can leverage the structure to produce both abstractive and extractive aspect-based summaries; and (c) that structure is particularly advantageous for summarizing long documents.
Categorization in the Wild: Generalizing Cognitive Models to Naturalistic Data across Languages
Categories such as animal or furniture are acquired at an early age and play an important role in processing, organizing, and communicating world knowledge.
Whodunnit? Crime Drama as a Case for Natural Language Understanding
In this paper we argue that crime drama exemplified in television programs such as CSI:Crime Scene Investigation is an ideal testbed for approximating real-world natural language understanding and the complex inferences associated with it.
Prosodic Features from Large Corpora of Child-Directed Speech as Predictors of the Age of Acquisition of Words
The impressive ability of children to acquire language is a widely studied phenomenon, and the factors influencing the pace and patterns of word learning remains a subject of active research.
Inducing Semantic Micro-Clusters from Deep Multi-View Representations of Novels
Automatically understanding the plot of novels is important both for informing literary scholarship and applications such as summarization or recommendation.
A Bayesian Model of Diachronic Meaning Change
Word meanings change over time and an automated procedure for extracting this information from text would be useful for historical exploratory studies, information retrieval or question answering.
