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Found 45 papers, 29 papers with code
UniMa at SemEval-2018 Task 7: Semantic Relation Extraction and Classification from Scientific Publications
Large repositories of scientific literature call for the development of robust methods to extract information from scholarly papers.
Investigating the Role of Argumentation in the Rhetorical Analysis of Scientific Publications with Neural Multi-Task Learning Models
Exponential growth in the number of scientific publications yields the need for effective automatic analysis of rhetorical aspects of scientific writing.
An Argument-Annotated Corpus of Scientific Publications
We analyze the annotated argumentative structures and investigate the relations between argumentation and other rhetorical aspects of scientific writing, such as discourse roles and citation contexts.
ArguminSci: A Tool for Analyzing Argumentation and Rhetorical Aspects in Scientific Writing
Argumentation is arguably one of the central features of scientific language.
Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word Vectors
In this work, we present a systematic study of biases encoded in distributional word vector spaces: we analyze how consistent the bias effects are across languages, corpora, and embedding models.
MinScIE: Citation-centered Open Information Extraction
Acknowledging the importance of citations in scientific literature, in this work we present MinScIE, an Open Information Extraction system which provides structured knowledge enriched with semantic information about citations.
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
In this work, we complement such distributional knowledge with external lexical knowledge, that is, we integrate the discrete knowledge on word-level semantic similarity into pretraining.
A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces
Moreover, we successfully transfer debiasing models, by means of cross-lingual embedding spaces, and remove or attenuate biases in distributional word vector spaces of languages that lack readily available bias specifications.
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers
Massively multilingual transformers pretrained with language modeling objectives (e. g., mBERT, XLM-R) have become a de facto default transfer paradigm for zero-shot cross-lingual transfer in NLP, offering unmatched transfer performance.
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers
Following the major success of neural language models (LMs) such as BERT or GPT-2 on a variety of language understanding tasks, recent work focused on injecting (structured) knowledge from external resources into these models.
The OpenCitations Data Model
A variety of schemas and ontologies are currently used for the machine-readable description of bibliographic entities and citations.
Digital Libraries
Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing
Though preceding work in computational argument quality (AQ) mostly focuses on assessing overall AQ, researchers agree that writers would benefit from feedback targeting individual dimensions of argumentation theory.
Creating a Domain-diverse Corpus for Theory-based Argument Quality Assessment
Computational models of argument quality (AQ) have focused primarily on assessing the overall quality or just one specific characteristic of an argument, such as its convincingness or its clarity.
AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings
Our analysis yields several interesting findings, e. g., that implicit gender bias in embeddings trained on Arabic news corpora steadily increases over time (between 2007 and 2017).
Self-Supervised Learning for Visual Summary Identification in Scientific Publications
Providing visual summaries of scientific publications can increase information access for readers and thereby help deal with the exponential growth in the number of scientific publications.
DebIE: A Platform for Implicit and Explicit Debiasing of Word Embedding Spaces
In this work, we present DebIE, the first integrated platform for (1) measuring and (2) mitigating bias in word embeddings.
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models
We use the evaluation framework to benchmark the widely used conversational DialoGPT model along with the adaptations of four debiasing methods.
MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting
In our work, we address this research gap by proposing a novel framework for CCA as a document-level context extraction and labeling task.
Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation
Despite extensive research efforts in recent years, computational argumentation (CA) remains one of the most challenging areas of natural language processing.
Diachronic Analysis of German Parliamentary Proceedings: Ideological Shifts through the Lens of Political Biases
We analyze bias in historical corpora as encoded in diachronic distributional semantic models by focusing on two specific forms of bias, namely a political (i. e., anti-communism) and racist (i. e., antisemitism) one.
Sustainable Modular Debiasing of Language Models
Unfair stereotypical biases (e. g., gender, racial, or religious biases) encoded in modern pretrained language models (PLMs) have negative ethical implications for widespread adoption of state-of-the-art language technology.
DS-TOD: Efficient Domain Specialization for Task Oriented Dialog
Recent work has shown that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining in downstream task-oriented dialog (TOD).
Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender
Based on our observations and ethical considerations, we define a series of desiderata for modeling pronouns in language technology.
Fair and Argumentative Language Modeling for Computational Argumentation
We employ our resource to assess the effect of argumentative fine-tuning and debiasing on the intrinsic bias found in transformer-based language models using a lightweight adapter-based approach that is more sustainable and parameter-efficient than full fine-tuning.
Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog
We then introduce a new framework for multilingual conversational specialization of pretrained language models (PrLMs) that aims to facilitate cross-lingual transfer for arbitrary downstream TOD tasks.
On the Limitations of Sociodemographic Adaptation with Transformers
We adapt the language representations for the sociodemographic dimensions of gender and age, using continuous language modeling and dynamic multi-task learning for adaptation, where we couple language modeling with the prediction of a sociodemographic class.
Back to the Future: On Potential Histories in NLP
Machine learning and NLP require the construction of datasets to train and fine-tune models.
Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers
Previous work showed that incorporating demographic factors can consistently improve performance for various NLP tasks with traditional NLP models.
SocioProbe: What, When, and Where Language Models Learn about Sociodemographics
Our results show that PLMs do encode these sociodemographics, and that this knowledge is sometimes spread across the layers of some of the tested PLMs.
Bridging Fairness and Environmental Sustainability in Natural Language Processing
Fairness and environmental impact are important research directions for the sustainable development of artificial intelligence.
What about em? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns
As 3rd-person pronoun usage shifts to include novel forms, e. g., neopronouns, we need more research on identity-inclusive NLP.
Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Models
Cutting-edge image generation has been praised for producing high-quality images, suggesting a ubiquitous future in a variety of applications.
Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting
However, the available NLP literature disagrees on the efficacy of this technique - it remains unclear for which tasks and scenarios it can help, and the role of the individual factors in sociodemographic prompting is still unexplored.
AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ
To address this, we propose the use of TikZ, a well-known abstract graphics language that can be compiled to vector graphics, as an intermediate representation of scientific figures.
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale
We introduce ScaLearn, a simple and highly parameter-efficient two-stage MTL method that capitalizes on the knowledge of the source tasks by learning a minimal set of scaling parameters that enable effective knowledge transfer to a target task.
A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation
In MT, this might lead to misgendered translations, resulting, among other harms, in the perpetuation of stereotypes and prejudices.
Values, Ethics, Morals? On the Use of Moral Concepts in NLP Research
For instance, we analyse what ethical theory an approach is based on, how this decision is justified, and what implications it entails.
Exploring Jiu-Jitsu Argumentation for Writing Peer Review Rebuttals
In this work, we are the first to explore Jiu-Jitsu argumentation for peer review by proposing the novel task of attitude and theme-guided rebuttal generation.
What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition
The knowledge encapsulated in a model is the core factor determining its final performance on downstream tasks.
Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ
Therefore, in this paper, we investigate the basic multilingual capabilities of state-of-the-art open LLMs beyond their intended use.
Argument Quality Assessment in the Age of Instruction-Following Large Language Models
The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like.
Robust Pronoun Use Fidelity with English LLMs: Are they Reasoning, Repeating, or Just Biased?
We find that while models can mostly faithfully reuse previously-specified pronouns in the presence of no distractors, they are significantly worse at processing she/her/her, singular they and neopronouns.
DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog
Recent work has shown that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining in downstream task-oriented dialog (TOD).
From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers
Massively multilingual transformers (MMTs) pretrained via language modeling (e. g., mBERT, XLM-R) have become a default paradigm for zero-shot language transfer in NLP, offering unmatched transfer performance.
Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals
Current language technology is ubiquitous and directly influences individuals’ lives worldwide.
