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Found 329 papers, 174 papers with code
Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks
Argument mining is a core technology for automating argument search in large document collections.
Lexical-semantic resources: yet powerful resources for automatic personality classification
In this paper, we aim to reveal the impact of lexical-semantic resources, used in particular for word sense disambiguation and sense-level semantic categorization, on automatic personality classification task.
Argumentation Mining in User-Generated Web Discourse
The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation.
Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks
We analyze the performance of encoder-decoder neural models and compare them with well-known established methods.
Parsing Argumentation Structures in Persuasive Essays
In this article, we present a novel approach for parsing argumentation structures.
Large-scale Multi-label Text Classification - Revisiting Neural Networks
Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer.
Cross-topic Argument Mining from Heterogeneous Sources
Argument mining is a core technology for automating argument search in large document collections.
Integrating Knowledge-Supported Search into the INCEpTION Annotation Platform
Annotating entity mentions and linking them to a knowledge resource are essential tasks in many domains.
Corpus-Driven Thematic Hierarchy Induction
Thematic role hierarchy is a widely used linguistic tool to describe interactions between semantic roles and their syntactic realizations.
Event Time Extraction with a Decision Tree of Neural Classifiers
We use this tree to incrementally infer, in a stepwise manner, at which time frame an event happened.
Generating Training Data for Semantic Role Labeling based on Label Transfer from Linked Lexical Resources
We present a new approach for generating role-labeled training data using Linked Lexical Resources, i. e., integrated lexical resources that combine several resources (e. g., Word-Net, FrameNet, Wiktionary) by linking them on the sense or on the role level.
Argumentation Quality Assessment: Theory vs. Practice
Argumentation quality is viewed differently in argumentation theory and in practical assessment approaches.
Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets
Previous models for the assessment of commitment towards a predicate in a sentence (also known as factuality prediction) were trained and tested against a specific annotated dataset, subsequently limiting the generality of their results.
Out-of-domain FrameNet Semantic Role Labeling
We create a novel test set for FrameNet SRL based on user-generated web text and find that the major bottleneck for out-of-domain FrameNet SRL is the frame identification step.
Metaheuristic Approaches to Lexical Substitution and Simplification
In this paper, we propose using metaheuristics{---}in particular, simulated annealing and the new D-Bees algorithm{---}to solve word sense disambiguation as an optimization problem within a knowledge-based lexical substitution system.
Recognizing Insufficiently Supported Arguments in Argumentative Essays
In this paper, we propose a new task for assessing the quality of natural language arguments.
A tool for extracting sense-disambiguated example sentences through user feedback
This paper describes an application system aimed to help lexicographers in the extraction of example sentences for a given headword based on its different senses.
Multimodal Frame Identification with Multilingual Evaluation
Our analysis shows that for the German data, textual representations are still competitive with multimodal ones.
Objective Function Learning to Match Human Judgements for Optimization-Based Summarization
Supervised summarization systems usually rely on supervision at the sentence or n-gram level provided by automatic metrics like ROUGE, which act as noisy proxies for human judgments.
ArgumenText: Searching for Arguments in Heterogeneous Sources
Argument mining is a core technology for enabling argument search in large corpora.
SemEval-2018 Task 12: The Argument Reasoning Comprehension Task
A natural language argument is composed of a claim as well as reasons given as premises for the claim.
A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning
Current methods for knowledge graph (KG) representation learning focus solely on the structure of the KG and do not exploit any kind of external information, such as visual and linguistic information corresponding to the KG entities.
SemEval-2017 Task 7: Detection and Interpretation of English Puns
A pun is a form of wordplay in which a word suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another word, for an intended humorous or rhetorical effect.
GraphDocExplore: A Framework for the Experimental Comparison of Graph-based Document Exploration Techniques
Many techniques to automatically extract different types of graphs, showing for example entities or concepts and different relationships between them, have been suggested.
BinLin: A Simple Method of Dependency Tree Linearization
Surface Realization Shared Task 2018 is a workshop on generating sentences from lemmatized sets of dependency triples.
One Size Fits All? A simple LSTM for non-literal token and construction-level classification
In this paper, we tackle four different tasks of non-literal language classification: token and construction level metaphor detection, classification of idiomatic use of infinitive-verb compounds, and classification of non-literal particle verbs.
Assessing SRL Frameworks with Automatic Training Data Expansion
We present the first experiment-based study that explicitly contrasts the three major semantic role labeling frameworks.
Distantly Supervised POS Tagging of Low-Resource Languages under Extreme Data Sparsity: The Case of Hittite
To our knowledge, this is the first attempt of statistical POS tagging of a cuneiform language.
Prediction of Frame-to-Frame Relations in the FrameNet Hierarchy with Frame Embeddings
Automatic completion of frame-to-frame (F2F) relations in the FrameNet (FN) hierarchy has received little attention, although they incorporate meta-level commonsense knowledge and are used in downstream approaches.
Learning to Score System Summaries for Better Content Selection Evaluation.
The evaluation of summaries is a challenging but crucial task of the summarization field.
Latest News in Computational Argumentation: Surfing on the Deep Learning Wave, Scuba Diving in the Abyss of Fundamental Questions
Can we reliably annotate convincingness of an argument?
A Web-based Tool for the Integrated Annotation of Semantic and Syntactic Structures
We introduce the third major release of WebAnno, a generic web-based annotation tool for distributed teams.
Task-Oriented Intrinsic Evaluation of Semantic Textual Similarity
Semantic Textual Similarity (STS) is a foundational NLP task and can be used in a wide range of tasks.
CNN- and LSTM-based Claim Classification in Online User Comments
When processing arguments in online user interactive discourse, it is often necessary to determine their bases of support.
Modeling Extractive Sentence Intersection via Subtree Entailment
Sentence intersection captures the semantic overlap of two texts, generalizing over paradigms such as textual entailment and semantic text similarity.
Abstractive Text Summarization
Natural Language Inference
+2
Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization
Concept-map-based multi-document summarization is a variant of traditional summarization that produces structured summaries in the form of concept maps.
Real-Time News Summarization with Adaptation to Media Attention
In particular, at times with high media attention, our approach exploits the redundancy in content to produce a more precise summary and avoid emitting redundant information.
Predicting the Difficulty of Language Proficiency Tests
Language proficiency tests are used to evaluate and compare the progress of language learners.
Dijkstra-WSA: A Graph-Based Approach to Word Sense Alignment
In this paper, we present Dijkstra-WSA, a novel graph-based algorithm for word sense alignment.
WordNet---Wikipedia---Wiktionary: Construction of a Three-way Alignment
The coverage and quality of conceptual information contained in lexical semantic resources is crucial for many tasks in natural language processing.
Lexical Substitution Dataset for German
This article describes a lexical substitution dataset for German.
UBY-LMF -- A Uniform Model for Standardizing Heterogeneous Lexical-Semantic Resources in ISO-LMF
We present UBY-LMF, an LMF-based model for large-scale, heterogeneous multilingual lexical-semantic resources (LSRs).
The Open Linguistics Working Group
This paper describes the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation (OKFN).
Pitfalls in the Evaluation of Sentence Embeddings
Our motivation is to challenge the current evaluation of sentence embeddings and to provide an easy-to-access reference for future research.
Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains
Many complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation.
Crowdsourcing a Large Dataset of Domain-Specific Context-Sensitive Semantic Verb Relations
We present a new large dataset of 12403 context-sensitive verb relations manually annotated via crowdsourcing.
Combining Semantic Annotation of Word Sense \& Semantic Roles: A Novel Annotation Scheme for VerbNet Roles on German Language Data
We present a VerbNet-based annotation scheme for semantic roles that we explore in an annotation study on German language data that combines word sense and semantic role annotation.
Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference
While recent progress on abstractive summarization has led to remarkably fluent summaries, factual errors in generated summaries still severely limit their use in practice.
FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning
Our proposed system FAMULUS helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data.
What do Deep Networks Like to Read?
Recent research towards understanding neural networks probes models in a top-down manner, but is only able to identify model tendencies that are known a priori.
Joint Wasserstein Autoencoders for Aligning Multimodal Embeddings
One of the key challenges in learning joint embeddings of multiple modalities, e. g. of images and text, is to ensure coherent cross-modal semantics that generalize across datasets.
Improving Generalization by Incorporating Coverage in Natural Language Inference
Finally, we show that using the coverage information is not only beneficial for improving the performance across different datasets of the same task.
When is ACL's Deadline? A Scientific Conversational Agent
Our conversational agent UKP-ATHENA assists NLP researchers in finding and exploring scientific literature, identifying relevant authors, planning or post-processing conference visits, and preparing paper submissions using a unified interface based on natural language inputs and responses.
Revisiting the Binary Linearization Technique for Surface Realization
End-to-end neural approaches have achieved state-of-the-art performance in many natural language processing (NLP) tasks.
Two Birds with One Stone: Investigating Invertible Neural Networks for Inverse Problems in Morphology
We show that they are able to recover the morphological input parameters, i. e., predicting the lemma (e. g., cat) or the morphological tags (e. g., Plural) when run in the reverse direction, without any significant performance drop in the forward direction, i. e., predicting the surface form (e. g., cats).
Analyzing Structures in the Semantic Vector Space: A Framework for Decomposing Word Embeddings
Word embeddings are rich word representations, which in combination with deep neural networks, lead to large performance gains for many NLP tasks.
Metaphoric Paraphrase Generation
This work describes the task of metaphoric paraphrase generation, in which we are given a literal sentence and are charged with generating a metaphoric paraphrase.
PuzzLing Machines: A Challenge on Learning From Small Data
To expose this problem in a new light, we introduce a challenge on learning from small data, PuzzLing Machines, which consists of Rosetta Stone puzzles from Linguistic Olympiads for high school students.
A Matter of Framing: The Impact of Linguistic Formalism on Probing Results
Deep pre-trained contextualized encoders like BERT (Delvin et al., 2019) demonstrate remarkable performance on a range of downstream tasks.
Low Resource Multi-Task Sequence Tagging -- Revisiting Dynamic Conditional Random Fields
We compare different models for low resource multi-task sequence tagging that leverage dependencies between label sequences for different tasks.
Improving Factual Consistency Between a Response and Persona Facts
Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with facts describing the speaker's persona.
Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs
We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation.
Ranked #5 on
KG-to-Text Generation
on AGENDA
Improving Robustness by Augmenting Training Sentences with Predicate-Argument Structures
Existing approaches to improve robustness against dataset biases mostly focus on changing the training objective so that models learn less from biased examples.
Ranking Creative Language Characteristics in Small Data Scenarios
We therefore adapt the DirectRanker to provide a new deep model for ranking creative language with small data.
The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes
Information Retrieval using dense low-dimensional representations recently became popular and showed out-performance to traditional sparse-representations like BM25.
Empirical Evaluation of Supervision Signals for Style Transfer Models
In this work we empirically compare the dominant optimization paradigms which provide supervision signals during training: backtranslation, adversarial training and reinforcement learning.
Focusing Knowledge-based Graph Argument Mining via Topic Modeling
We use a topic model to extract topic- and sentence-specific evidence from the structured knowledge base Wikidata, building a graph based on the cosine similarity between the entity word vectors of Wikidata and the vector of the given sentence.
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.
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.
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.
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).
On the rate of convergence of a classifier based on a Transformer encoder
Pattern recognition based on a high-dimensional predictor is considered.
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.
Composing Structure-Aware Batches for Pairwise Sentence Classification
Identifying the relation between two sentences requires datasets with pairwise annotations.
Diversity Over Size: On the Effect of Sample and Topic Sizes for Argument Mining Datasets
The task of Argument Mining, that is extracting argumentative sentences 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 argumentative sentences requires expert knowledge.
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.
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.
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.
An Inclusive Notion of Text
Natural language processing (NLP) researchers develop models of grammar, meaning and communication based on written text.
Semantic Similarity Models for Depression Severity Estimation
Depressive disorders constitute a severe public health issue worldwide.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Analyzing Dataset Annotation Quality Management in the Wild
A majority of the annotated publications apply good or excellent quality management.
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.
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?
How are Prompts Different in Terms of Sensitivity?
In-context learning (ICL) has become one of the most popular learning paradigms.
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.
Document Structure in Long Document Transformers
Long documents often exhibit structure with hierarchically organized elements of different functions, such as section headers and paragraphs.
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.
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.
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.
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}.
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.
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.
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.
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.
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).
Holmes: Benchmark the Linguistic Competence of Language Models
We introduce Holmes, a benchmark to assess the linguistic competence of language models (LMs) - their ability to grasp linguistic phenomena.
Multimodal Grounding for Language Processing
This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language.
Cross-Lingual Argumentative Relation Identification: from English to Portuguese
Argument mining aims to detect and identify argument structures from textual resources.
Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization
Concept map-based multi-document summarization has recently been proposed as a variant of the traditional summarization task with graph-structured summaries.
Sense-annotating a Lexical Substitution Data Set with Ubyline
Also introduced in this paper is Ubyline, the web application used to produce the sense annotations.
Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning
Most humour processing systems to date make at best discrete, coarse-grained distinctions between the comical and the conventional, yet such notions are better conceptualized as a broad spectrum.
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.
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.
Learning from Emotions, Demographic Information and Implicit User Feedback in Task-Oriented Document-Grounded Dialogues
The success of task-oriented and document-grounded dialogue systems depends on users accepting and enjoying using them.
Challenges in the Automatic Analysis of Students' Diagnostic Reasoning
We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification.
PD3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection
We consider unsupervised cross-lingual transfer on two tasks, viz., sentence-level argumentation mining and standard POS tagging.
Semi-automatic Detection of Cross-lingual Marketing Blunders based on Pragmatic Label Propagation in Wiktionary
We introduce the task of detecting cross-lingual marketing blunders, which occur if a trade name resembles an inappropriate or negatively connotated word in a target language.
Preference-based Interactive Multi-Document Summarisation
Interactive NLP is a promising paradigm to close the gap between automatic NLP systems and the human upper bound.
Improving QA Generalization by Concurrent Modeling of Multiple Biases
Our framework weights each example based on the biases it contains and the strength of those biases in the training data.
The challenges of temporal alignment on Twitter during crises
Language use changes over time, and this impacts the effectiveness of NLP systems.
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.
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.
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.
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.
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.
Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection
In this paper we investigate multi-task learning for related non-literal language phenomena.
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.
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.
A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd
The study of argumentation and the development of argument mining tools depends on the availability of annotated data, which is challenging to obtain in sufficient quantity and quality.
Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation
For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user's needs.
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts
The ultimate challenge is dealing with under-resourced languages not covered at all by the models and written in scripts unseen during pretraining.
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.
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.
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.
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.
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.
A domain-agnostic approach for opinion prediction on speech
We explore a domain-agnostic approach for analyzing speech with the goal of opinion prediction.
Bridging the gap between extractive and abstractive summaries: Creation and evaluation of coherent extracts from heterogeneous sources
Coherent extracts are a novel type of summary combining the advantages of manually created abstractive summaries, which are fluent but difficult to evaluate, and low-quality automatically created extractive summaries, which lack coherence and structure.
LINSPECTOR WEB: A Multilingual Probing Suite for Word Representations
We present LINSPECTOR WEB, an open source multilingual inspector to analyze word representations.
