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Found 364 papers, 207 papers with code
IMPLI: Investigating NLI Models’ Performance on Figurative Language
Natural language inference (NLI) has been widely used as a task to train and evaluate models for language understanding.
Composing Structure-Aware Batches for Pairwise Sentence Classification
Identifying the relation between two sentences requires datasets with pairwise annotations.
Human-In-The-LoopEntity Linking for Low Resource Domains
Entity linking (EL) is concerned with disambiguating entity mentions in a text against knowledge bases (KB).
Evaluating Coreference Resolvers on Community-based Question Answering: From Rule-based to State of the Art
The results of our extrinsic evaluation show that while there is a significant difference between the performance of the rule-based system vs. state-of-the-art neural model on coreference resolution datasets, we do not observe a considerable difference on their impact on downstream models.
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning
Learning sentence embeddings often requires a large amount of labeled data.
A Neural Graph-based Local Coherence Model
Entity grids and entity graphs are two frameworks for modeling local coherence.
Exploring Metaphoric Paraphrase Generation
Metaphor generation is a difficult task, and has seen tremendous improvement with the advent of deep pretrained models.
Event Coreference Data (Almost) for Free: Mining Hyperlinks from Online News
Cross-document event coreference resolution (CDCR) is the task of identifying which event mentions refer to the same events throughout a collection of documents.
FIRE: Fact-checking with Iterative Retrieval and Verification
The typical approach to fact-checking these atomic claims involves retrieving a fixed number of pieces of evidence, followed by a verification step.
Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions
We instantiate this framework in edit intent classification (EIC), a challenging and underexplored classification task.
Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Science
Large Language Models (LLMs) have ushered in a transformative era in Natural Language Processing (NLP), reshaping research and extending NLP's influence to other fields of study.
The Nature of NLP: Analyzing Contributions in NLP Papers
In this work, we quantitatively investigate what constitutes NLP by examining research papers.
The Lou Dataset -- Exploring the Impact of Gender-Fair Language in German Text Classification
Gender-fair language, an evolving German linguistic variation, fosters inclusion by addressing all genders or using neutral forms.
Efficient Performance Tracking: Leveraging Large Language Models for Automated Construction of Scientific Leaderboards
Building on this dataset, we propose three experimental settings that simulate real-world scenarios where TDM triples are fully defined, partially defined, or undefined during leaderboard construction.
RIRAG: Regulatory Information Retrieval and Answer Generation
Regulatory documents, issued by governmental regulatory bodies, establish rules, guidelines, and standards that organizations must adhere to for legal compliance.
Diagnostic Reasoning in Natural Language: Computational Model and Application
To close this gap, we investigate diagnostic abductive reasoning (DAR) in the context of language-grounded tasks (NL-DAR).
DOCE: Finding the Sweet Spot for Execution-Based Code Generation
Recently, a diverse set of decoding and reranking procedures have been shown effective for LLM-based code generation.
Grounding Fallacies Misrepresenting Scientific Publications in Evidence
Health-related misinformation claims often falsely cite a credible biomedical publication as evidence, which superficially appears to support the false claim.
"Image, Tell me your story!" Predicting the original meta-context of visual misinformation
By explaining what is actually true about the image, fact-checkers can better detect misinformation, focus their efforts on check-worthy visual content, engage in counter-messaging before misinformation spreads widely, and make their explanation more convincing.
Problem Solving Through Human-AI Preference-Based Cooperation
While there is a widespread belief that artificial general intelligence (AGI) -- or even superhuman AI -- is imminent, complex problems in expert domains are far from being solved.
LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection
Category (iii) aims to detect attempts to obfuscate the fact that a text was machine-generated, while category (iv) looks for cases where the LLM was used to polish a human-written text, which is typically acceptable in academic writing, but not in education.
OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs
The increased use of large language models (LLMs) across a variety of real-world applications calls for automatic tools to check the factual accuracy of their outputs, as LLMs often hallucinate.
A Course Shared Task on Evaluating LLM Output for Clinical Questions
This paper presents a shared task that we organized at the Foundations of Language Technology (FoLT) course in 2023/2024 at the Technical University of Darmstadt, which focuses on evaluating the output of Large Language Models (LLMs) in generating harmful answers to health-related clinical questions.
Overview of PerpectiveArg2024: The First Shared Task on Perspective Argument Retrieval
Argument retrieval is the task of finding relevant arguments for a given query.
Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual Alignment
Another limitation of multilingual sentence encoders is the trade-off between monolingual and cross-lingual performance.
$\textit{GeoHard}$: Towards Measuring Class-wise Hardness through Modelling Class Semantics
Recent advances in measuring hardness-wise properties of data guide language models in sample selection within low-resource scenarios.
Localizing and Mitigating Errors in Long-form Question Answering
This work introduces HaluQuestQA, the first hallucination dataset with localized error annotations for human-written and model-generated LFQA answers.
Robust Utility-Preserving Text Anonymization Based on Large Language Models
Text anonymization is crucial for sharing sensitive data while maintaining privacy.
InferAct: Inferring Safe Actions for LLM-Based Agents Through Preemptive Evaluation and Human Feedback
A crucial requirement for deploying LLM-based agents in real-life applications is the robustness against risky or even irreversible mistakes.
$\texttt{MixGR}$: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity
Recent studies show the growing significance of document retrieval in the generation of LLMs, i. e., RAG, within the scientific domain by bridging their knowledge gap.
Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors
We show empirically that finding the mistake in a student solution is challenging for current models.
HDT: Hierarchical Document Transformer
We address the technical challenge of implementing HDT's sample-dependent hierarchical attention pattern by developing a novel sparse attention kernel that considers the hierarchical structure of documents.
Systematic Task Exploration with LLMs: A Study in Citation Text Generation
Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks.
LLM Roleplay: Simulating Human-Chatbot Interaction
The development of chatbots requires collecting a large number of human-chatbot dialogues to reflect the breadth of users' sociodemographic backgrounds and conversational goals.
Fine-Tuning with Divergent Chains of Thought Boosts Reasoning Through Self-Correction in Language Models
In fact, it has been found that instruction tuning on these intermediary reasoning steps improves model performance.
M2QA: Multi-domain Multilingual Question Answering
While adapting NLP models to new languages within a single domain, or to new domains within a single language, is widely studied, research in joint adaptation is hampered by the lack of evaluation datasets.
DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs
Answering Questions over Knowledge Graphs (KGQA) is key to well-functioning autonomous language agents in various real-life applications.
SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models
In this work, we present a comprehensive study of the capability of current state-of-the-art large language models (LLMs) on spatial reasoning.
Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art
The surge of interest in culturally aware and adapted Natural Language Processing (NLP) has inspired much recent research.
Missci: Reconstructing Fallacies in Misrepresented Science
Unlike previous fallacy detection datasets, Missci (i) focuses on implicit fallacies between the relevant content of the cited publication and the inaccurate claim, and (ii) requires models to verbalize the fallacious reasoning in addition to classifying it.
Re3: A Holistic Framework and Dataset for Modeling Collaborative Document Revision
Collaborative review and revision of textual documents is the core of knowledge work and a promising target for empirical analysis and NLP assistance.
What Can Natural Language Processing Do for Peer Review?
We hope that our work will help set the agenda for research in machine-assisted scientific quality control in the age of AI, within the NLP community and beyond.
Holmes: A Benchmark to Assess the Linguistic Competence of Language Models
We introduce Holmes, a new benchmark designed to assess language models (LMs) linguistic competence - their unconscious understanding of linguistic phenomena.
SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection
The task attracted a large number of participants: subtask A monolingual (126), subtask A multilingual (59), subtask B (70), and subtask C (30).
Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning
While Large Language Models (LLMs) exhibit remarkable capabilities in zero-shot and few-shot scenarios, they often require computationally prohibitive sizes.
Constrained C-Test Generation via Mixed-Integer Programming
This work proposes a novel method to generate C-Tests; a deviated form of cloze tests (a gap filling exercise) where only the last part of a word is turned into a gap.
Early Period of Training Impacts Out-of-Distribution Generalization
Prior research has found that differences in the early period of neural network training significantly impact the performance of in-distribution (ID) tasks.
IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators
In particular, most mainstream Code-LMs have been pre-trained on source code files alone.
Multimodal Large Language Models to Support Real-World Fact-Checking
To the best of our knowledge, we are the first to evaluate MLLMs for real-world fact-checking.
Socratic Reasoning Improves Positive Text Rewriting
In this work, we address this gap by augmenting open-source datasets for positive text rewriting with synthetically-generated Socratic rationales using a novel framework called \textsc{SocraticReframe}.
Variational Learning is Effective for Large Deep Networks
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks.
Triple-Encoders: Representations That Fire Together, Wire Together
Search-based dialog models typically re-encode the dialog history at every turn, incurring high cost.
M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection
The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels.
Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon
Improving multilingual language models capabilities in low-resource languages is generally difficult due to the scarcity of large-scale data in those languages.
Dive into the Chasm: Probing the Gap between In- and Cross-Topic Generalization
Pre-trained language models (LMs) perform well in In-Topic setups, where training and testing data come from the same topics.
Document Structure in Long Document Transformers
Long documents often exhibit structure with hierarchically organized elements of different functions, such as section headers and paragraphs.
Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs
Reasoning is a fundamental component of language understanding.
Learning from Implicit User Feedback, Emotions and Demographic Information in Task-Oriented and Document-Grounded Dialogues
Implicit user feedback, user emotions and demographic information have shown to be promising sources for improving the accuracy and user engagement of responses generated by dialogue systems.
Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning
We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models.
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs.
A Survey of Confidence Estimation and Calibration in Large Language Models
Assessing their confidence and calibrating them across different tasks can help mitigate risks and enable LLMs to produce better generations.
How are Prompts Different in Terms of Sensitivity?
In-context learning (ICL) has become one of the most popular learning paradigms.
A Template Is All You Meme
Here, to aid understanding of memes, we release a knowledge base of memes and information found on www. knowyourmeme. com, which we call the Know Your Meme Knowledge Base (KYMKB), composed of more than 54, 000 images.
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.
AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot Classification
As a solution, we propose AdaSent, which decouples SEPT from DAPT by training a SEPT adapter on the base PLM.
Learning From Free-Text Human Feedback -- Collect New Datasets Or Extend Existing Ones?
Learning from free-text human feedback is essential for dialog systems, but annotated data is scarce and usually covers only a small fraction of error types known in conversational AI.
Model Merging by Uncertainty-Based Gradient Matching
Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail?
Measuring Pointwise $\mathcal{V}$-Usable Information In-Context-ly
In-context learning (ICL) is a new learning paradigm that has gained popularity along with the development of large language models.
Are Multilingual LLMs Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayings
Large language models (LLMs) are highly adept at question answering and reasoning tasks, but when reasoning in a situational context, human expectations vary depending on the relevant cultural common ground.
How to Handle Different Types of Out-of-Distribution Scenarios in Computational Argumentation? A Comprehensive and Fine-Grained Field Study
Our findings challenge the previously asserted general superiority of in-context learning (ICL) for OOD.
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration
In recent years, large language models (LLMs) have shown remarkable capabilities at scale, particularly at generating text conditioned on a prompt.
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.
Are Emergent Abilities in Large Language Models just In-Context Learning?
Large language models, comprising billions of parameters and pre-trained on extensive web-scale corpora, have been claimed to acquire certain capabilities without having been specifically trained on them.
SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval
In this work, we provide SPRINT, a unified Python toolkit based on Pyserini and Lucene, supporting a common interface for evaluating neural sparse retrieval.
Analyzing Dataset Annotation Quality Management in the Wild
A majority of the annotated publications apply good or excellent quality management.
Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research
Many recent improvements in NLP stem from the development and use of large pre-trained language models (PLMs) with billions of parameters.
UKP-SQuARE: An Interactive Tool for Teaching Question Answering
To evaluate the effectiveness of UKP-SQuARE in teaching scenarios, we adopted it in a postgraduate NLP course and surveyed the students after the course.
Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation
These models also suffer from posterior collapse, i. e., the decoder tends to ignore latent variables and directly access information captured in the encoder through the cross-attention mechanism.
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection
These results show that the problem is far from solved and that there is a lot of room for improvement.
DAPR: A Benchmark on Document-Aware Passage Retrieval
This drives us to build a benchmark for this task including multiple datasets from heterogeneous domains.
MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems
While automatic dialogue tutors hold great potential in making education personalized and more accessible, research on such systems has been hampered by a lack of sufficiently large and high-quality datasets.
A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?
In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques.
Learning to Reason over Scene Graphs: A Case Study of Finetuning GPT-2 into a Robot Language Model for Grounded Task Planning
Long-horizon task planning is essential for the development of intelligent assistive and service robots.
Lessons Learned from a Citizen Science Project for Natural Language Processing
Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP.
Romanization-based Large-scale Adaptation of Multilingual Language Models
In order to boost the capacity of mPLMs to deal with low-resource and unseen languages, we explore the potential of leveraging transliteration on a massive scale.
UKP-SQuARE v3: A Platform for Multi-Agent QA Research
To ease research in multi-agent models, we extend UKP-SQuARE, an online platform for QA research, to support three families of multi-agent systems: i) agent selection, ii) early-fusion of agents, and iii) late-fusion of agents.
Elastic Weight Removal for Faithful and Abstractive Dialogue Generation
We evaluate our method -- using different variants of Flan-T5 as a backbone language model -- on multiple datasets for information-seeking dialogue generation and compare our method with state-of-the-art techniques for faithfulness, such as CTRL, Quark, DExperts, and Noisy Channel reranking.
Rediscovering Hashed Random Projections for Efficient Quantization of Contextualized Sentence Embeddings
Training and inference on edge devices often requires an efficient setup due to computational limitations.
CARE: Collaborative AI-Assisted Reading Environment
Recent years have seen impressive progress in AI-assisted writing, yet the developments in AI-assisted reading are lacking.
Like a Good Nearest Neighbor: Practical Content Moderation and Text Classification
Few-shot text classification systems have impressive capabilities but are infeasible to deploy and use reliably due to their dependence on prompting and billion-parameter language models.
Opportunities and Challenges in Neural Dialog Tutoring
Designing dialog tutors has been challenging as it involves modeling the diverse and complex pedagogical strategies employed by human tutors.
FUN with Fisher: Improving Generalization of Adapter-Based Cross-lingual Transfer with Scheduled Unfreezing
Our experiments reveal that scheduled unfreezing induces different learning dynamics compared to standard fine-tuning, and provide evidence that the dynamics of Fisher Information during training correlate with cross-lingual generalization performance.
Python Code Generation by Asking Clarification Questions
We hypothesize that the under-specification of a natural language description can be resolved by asking clarification questions.
CiteBench: A benchmark for Scientific Citation Text Generation
To address this challenge, we propose CiteBench: a benchmark for citation text generation that unifies multiple diverse datasets and enables standardized evaluation of citation text generation models across task designs and domains.
NLP meets psychotherapy: Using predicted client emotions and self-reported client emotions to measure emotional coherence
No study has examined EC between the subjective experience of emotions and emotion expression in therapy or whether this coherence is associated with clients' well being.
GDPR Compliant Collection of Therapist-Patient-Dialogues
While these conversations are part of the daily routine of clinicians, gathering them is usually hindered by various ethical (purpose of data usage), legal (data privacy) and technical (data formatting) limitations.
Semantic Similarity Models for Depression Severity Estimation
Depressive disorders constitute a severe public health issue worldwide.
NLPeer: A Unified Resource for the Computational Study of Peer Review
Peer review constitutes a core component of scholarly publishing; yet it demands substantial expertise and training, and is susceptible to errors and biases.
An Inclusive Notion of Text
Natural language processing (NLP) researchers develop models of grammar, meaning and communication based on written text.
Contextual information integration for stance detection via cross-attention
Stance detection deals with identifying an author's stance towards a target.
Effective Cross-Task Transfer Learning for Explainable Natural Language Inference with T5
We compare sequential fine-tuning with a model for multi-task learning in the context where we are interested in boosting performance on two tasks, one of which depends on the other.
Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation
In our analysis, we show that, by design, existing NLP task definitions for fact-checking cannot refute misinformation as professional fact-checkers do for the majority of claims.
Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking
Pairing a lexical retriever with a neural re-ranking model has set state-of-the-art performance on large-scale information retrieval datasets.
One does not fit all! On the Complementarity of Vision Encoders for Vision and Language Tasks
While many VEs -- of different architectures, trained on different data and objectives -- are publicly available, they are not designed for the downstream V+L tasks.
The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification
First, the unexplored combination of the cross-encoder architecture (with parameterized similarity scoring function) and episodic meta-learning consistently yields the best FSIC performance.
Efficient Methods for Natural Language Processing: A Survey
Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows.
Transformers with Learnable Activation Functions
In contrast to conventional, predefined activation functions, RAFs can adaptively learn optimal activation functions during training according to input data.
UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA
In this paper, we introduce SQuARE v2, the new version of SQuARE, to provide an explainability infrastructure for comparing models based on methods such as saliency maps and graph-based explanations.
TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation
We evaluate TexPrax in a user-study with German factory employees who ask their colleagues for solutions on problems that arise during their daily work.
Mining Legal Arguments in Court Decisions
Identifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field.
Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future
While researchers show that their approaches work well on their newly introduced datasets, they rarely compare their methods to previous work or on the same datasets.
Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasets
The task of Argument Mining, that is extracting and classifying argument components for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large Argument Mining datasets are rare and recognition of argument components requires expert knowledge.
Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language Models
In this paper, we propose a new extended pretraining approach called Arithmetic-Based Pretraining that jointly addresses both in one extended pretraining step without requiring architectural changes or pretraining from scratch.
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization
In this work, we show that NLI models can be effective for this task when the training data is augmented with high-quality task-oriented examples.
Abstractive Text Summarization
Natural Language Inference
+1
Adaptable Adapters
The resulting adapters (a) contain about 50% of the learning parameters of the standard adapter and are therefore more efficient at training and inference, and require less storage space, and (b) achieve considerably higher performances in low-data settings.
Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review
While existing NLP studies focus on the analysis of individual texts, editorial assistance often requires modeling interactions between pairs of texts -- yet general frameworks and datasets to support this scenario are missing.
FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations
Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world applications.
UKP-SQUARE: An Online Platform for Question Answering Research
Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e. g., extractive, abstractive), require different model architectures (e. g., generative, discriminative), and setups (e. g., with or without retrieval).
Delving Deeper into Cross-lingual Visual Question Answering
2) We analyze cross-lingual VQA across different question types of varying complexity for different multilingual multimodal Transformers, and identify question types that are the most difficult to improve on.
ArgSciChat: A Dataset for Argumentative Dialogues on Scientific Papers
The applications of conversational agents for scientific disciplines (as expert domains) are understudied due to the lack of dialogue data to train such agents.
Ranked #1 on
Fact Selection
on ArgSciChat
Yes-Yes-Yes: Proactive Data Collection for ACL Rolling Review and Beyond
The shift towards publicly available text sources has enabled language processing at unprecedented scale, yet leaves under-serviced the domains where public and openly licensed data is scarce.
Addressing the Challenges of Cross-Lingual Hate Speech Detection
The goal of hate speech detection is to filter negative online content aiming at certain groups of people.
GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval
This limits the usage of dense retrieval approaches to only a few domains with large training datasets.
Ranked #9 on
Zero-shot Text Search
on BEIR
MetaQA: Combining Expert Agents for Multi-Skill Question Answering
The recent explosion of question answering (QA) datasets and models has increased the interest in the generalization of models across multiple domains and formats by either training on multiple datasets or by combining multiple models.
On the rate of convergence of a classifier based on a Transformer encoder
Pattern recognition based on a high-dimensional predictor is considered.
xGQA: Cross-Lingual Visual Question Answering
In this work, we address this gap and provide xGQA, a new multilingual evaluation benchmark for the visual question answering task.
TxT: Crossmodal End-to-End Learning with Transformers
Reasoning over multiple modalities, e. g. in Visual Question Answering (VQA), requires an alignment of semantic concepts across domains.
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning
Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream tasks as a language modeling problem.
Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation
Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR.
Assisting Decision Making in Scholarly Peer Review: A Preference Learning Perspective
Peer review is the primary means of quality control in academia; as an outcome of a peer review process, program and area chairs make acceptance decisions for each paper based on the review reports and scores they received.
AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters
The open-access dissemination of pretrained language models through online repositories has led to a democratization of state-of-the-art natural language processing (NLP) research.
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.
Annotation Curricula to Implicitly Train Non-Expert Annotators
Annotation studies often require annotators to familiarize themselves with the task, its annotation scheme, and the data domain.
Metaphor Generation with Conceptual Mappings
Guided by conceptual metaphor theory, we propose to control the generation process by encoding conceptual mappings between cognitive domains to generate meaningful metaphoric expressions.
Investigating label suggestions for opinion mining in German Covid-19 social media
This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data.
The challenges of temporal alignment on Twitter during crises
Language use changes over time, and this impacts the effectiveness of NLP systems.
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval.
Ranked #1 on
Argument Retrieval
on ArguAna (BEIR)
Learning to Reason for Text Generation from Scientific Tables
In this paper, we introduce SciGen, a new challenge dataset for the task of reasoning-aware data-to-text generation consisting of tables from scientific articles and their corresponding descriptions.
What to Pre-Train on? Efficient Intermediate Task Selection
Our best methods achieve an average Regret@3 of less than 1% across all target tasks, demonstrating that we are able to efficiently identify the best datasets for intermediate training.
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning
Learning sentence embeddings often requires a large amount of labeled data.
Ranked #1 on
Re-Ranking
on AskUbuntu
TWEAC: Transformer with Extendable QA Agent Classifiers
We argue that there exist a wide range of specialized QA agents in literature.
AmbiFC: Fact-Checking Ambiguous Claims with Evidence
Automated fact-checking systems verify claims against evidence to predict their veracity.
Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval
Current state-of-the-art approaches to cross-modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image.
Structural Adapters in Pretrained Language Models for AMR-to-text Generation
Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation.
Ranked #1 on
Data-to-Text Generation
on AMR3.0