Search Results for author:
Found 328 papers, 173 papers with code
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10, 000 sentences requires about 50 million inference computations (~65 hours) with BERT.
Ranked #5 on
Linear-Probe Classification
on SentEval
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
The training is based on the idea that a translated sentence should be mapped to the same location in the vector space as the original sentence.
Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks
Bi-encoders, on the other hand, require substantial training data and fine-tuning over the target task to achieve competitive performance.
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
AdapterHub: A Framework for Adapting Transformers
We propose AdapterHub, a framework that allows dynamic "stitching-in" of pre-trained adapters for different tasks and languages.
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.
AdapterFusion: Non-Destructive Task Composition for Transfer Learning
We show that by separating the two stages, i. e., knowledge extraction and knowledge composition, the classifier can effectively exploit the representations learned from multiple tasks in a non-destructive manner.
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
The main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer.
Ranked #5 on
Cross-Lingual Transfer
on XCOPA
(using extra training data)
AdapterDrop: On the Efficiency of Adapters in Transformers
Massively pre-trained transformer models are computationally expensive to fine-tune, slow for inference, and have large storage requirements.
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance.
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)
Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approaches
In this publication, we show that there is a high risk that a statistical significance in this type of evaluation is not due to a superior learning approach.
Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging
In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches.
The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation
We introduce INCEpTION, a new annotation platform for tasks including interactive and semantic annotation (e. g., concept linking, fact linking, knowledge base population, semantic frame annotation).
Alternative Weighting Schemes for ELMo Embeddings
We evaluate different methods that combine the three vectors from the language model in order to achieve the best possible performance in downstream NLP tasks.
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
Context-Aware Representations for Knowledge Base Relation Extraction
We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation.
Ranked #1 on
Relation Extraction
on Wikipedia-Wikidata relations
Neural End-to-End Learning for Computational Argumentation Mining
Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results.
Investigating Pretrained Language Models for Graph-to-Text Generation
We show that the PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further.
Ranked #1 on
KG-to-Text Generation
on WebNLG (All)
Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations
Here, we generalize the concept of average word embeddings to power mean word embeddings.
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
The most approaches to Knowledge Base Question Answering are based on semantic parsing.
Ranked #1 on
Knowledge Base Question Answering
on WebQSP-WD
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.
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations.
Ranked #1 on
Graph-to-Sequence
on WebNLG
Classification and Clustering of Arguments with Contextualized Word Embeddings
We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search.
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
On this basis, we present a new challenging task, the argument reasoning comprehension task.
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).
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.
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.
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.
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.
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.
Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories
We use the Wikidata knowledge base and available question answering datasets to create benchmarks for entity linking on question answering data.
Ranked #2 on
Entity Linking
on WebQSP-WD
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.
C4Corpus: Multilingual Web-size Corpus with Free License
Large Web corpora containing full documents with permissive licenses are crucial for many NLP tasks.
A Retrospective Analysis of the Fake News Challenge Stance Detection Task
To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods.
UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification
The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text.
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.
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.
Better Rewards Yield Better Summaries: Learning to Summarise Without References
Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarisation systems, the RL systems using our learned rewards during training generate summarieswith higher human ratings.
Stance Detection Benchmark: How Robust Is Your Stance Detection?
Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim and has become a key component in applications like fake news detection, claim validation, and argument search.
Multi-Task Learning for Argumentation Mining in Low-Resource Settings
We investigate whether and where multi-task learning (MTL) can improve performance on NLP problems related to argumentation mining (AM), in particular argument component identification.
A Consolidated Open Knowledge Representation for Multiple Texts
We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner.
EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION
From these approaches, we created an ensemble of differently hyper-parameterized systems, achieving a micro-F1-score of 0. 63 on the test data.
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.
Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings
Therefore, we propose a novel semi-supervised framework, which models shared information between domains and domain-specific information separately.
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.
From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains
Entity linking (EL) is concerned with disambiguating entity mentions in a text against knowledge bases (KB).
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning
Learning sentence embeddings often requires a large amount of labeled data.
Are Emergent Abilities in Large Language Models just In-Context Learning?
Large language models have exhibited emergent abilities, demonstrating exceptional performance across diverse tasks for which they were not explicitly trained, including those that require complex reasoning abilities.
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
A Retrospective Analysis of the Fake News Challenge Stance-Detection Task
To date, there is no in-depth analysis paper to critically discuss FNC-1{'}s experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods.
Aspect-Controlled Neural Argument Generation
In this work, we train a language model for argument generation that can be controlled on a fine-grained level to generate sentence-level arguments for a given topic, stance, and aspect.
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.
Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks
Activation functions play a crucial role in neural networks because they are the nonlinearities which have been attributed to the success story of deep learning.
Predicting Research Trends From Arxiv
By extrapolation, we predict that these topics will remain lead problems/approaches in their fields in the short- and mid-term.
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models
In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolingual task performance.
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.
A Bayesian Approach for Sequence Tagging with Crowds
Current methods for sequence tagging, a core task in NLP, are data hungry, which motivates the use of crowdsourcing as a cheap way to obtain labelled data.
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
Visual modifications to text are often used to obfuscate offensive comments in social media (e. g., "! d10t") or as a writing style ("1337" in "leet speak"), among other scenarios.
Does My Rebuttal Matter? Insights from a Major NLP Conference
We then focus on the role of the rebuttal phase, and propose a novel task to predict after-rebuttal (i. e., final) scores from initial reviews and author responses.
Towards Debiasing NLU Models from Unknown Biases
Recently proposed debiasing methods are shown to be effective in mitigating this tendency.
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.
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.
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.
E2E NLG Challenge: Neural Models vs. Templates
E2E NLG Challenge is a shared task on generating restaurant descriptions from sets of key-value pairs.
Ranked #11 on
Data-to-Text Generation
on E2E NLG Challenge
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance
Models for natural language understanding (NLU) tasks often rely on the idiosyncratic biases of the dataset, which make them brittle against test cases outside the training distribution.
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.
LINSPECTOR: Multilingual Probing Tasks for Word Representations
We present a reusable methodology for creation and evaluation of such tests in a multilingual setting.
Enhancing AMR-to-Text Generation with Dual Graph Representations
Generating text from graph-based data, such as Abstract Meaning Representation (AMR), is a challenging task due to the inherent difficulty in how to properly encode the structure of a graph with labeled edges.
Ranked #11 on
AMR-to-Text Generation
on LDC2017T10
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.
Fine-Grained Argument Unit Recognition and Classification
In this work, we argue that the task should be performed on a more fine-grained level of sequence labeling.
DAPR: A Benchmark on Document-Aware Passage Retrieval
This drives us to build a benchmark for this task including multiple datasets from heterogeneous domains.
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.
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!
Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually.
Dialogue Coherence Assessment Without Explicit Dialogue Act Labels
Recent dialogue coherence models use the coherence features designed for monologue texts, e. g. nominal entities, to represent utterances and then explicitly augment them with dialogue-relevant features, e. g., dialogue act labels.
MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale
We investigate the model performances on nine benchmarks of answer selection and question similarity tasks, and show that all 140 models transfer surprisingly well, where the large majority of models substantially outperforms common IR baselines.
AmbiFC: Fact-Checking Ambiguous Claims with Evidence
Automated fact-checking systems verify claims against evidence to predict their veracity.
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.
Before Name-calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation
Arguing without committing a fallacy is one of the main requirements of an ideal debate.
What is the Essence of a Claim? Cross-Domain Claim Identification
Argument mining has become a popular research area in NLP.
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning
The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries.
Interactive Instance-based Evaluation of Knowledge Base Question Answering
Most approaches to Knowledge Base Question Answering are based on semantic parsing.
Finding Convincing Arguments Using Scalable Bayesian Preference Learning
We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard rat- ings or rankings.
Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps
Concept maps can be used to concisely represent important information and bring structure into large document collections.
LSDSem 2017: Exploring Data Generation Methods for the Story Cloze Test
The Story Cloze test is a recent effort in providing a common test scenario for text understanding systems.
Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference Learning
The inability to quantify key aspects of creative language is a frequent obstacle to natural language understanding.
Scalable Bayesian Preference Learning for Crowds
As previous solutions based on Gaussian processes do not scale to large numbers of users, items or pairwise labels, we propose a stochastic variational inference approach that limits computational and memory costs.
TWEAC: Transformer with Extendable QA Agent Classifiers
We argue that there exist a wide range of specialized QA agents in literature.
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.
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.
Argotario: Computational Argumentation Meets Serious Games
An important skill in critical thinking and argumentation is the ability to spot and recognize fallacies.
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.
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.
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.
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.
Frame- and Entity-Based Knowledge for Common-Sense Argumentative Reasoning
Common-sense argumentative reasoning is a challenging task that requires holistic understanding of the argumentation where external knowledge about the world is hypothesized to play a key role.
Generalizing Cross-Document Event Coreference Resolution Across Multiple Corpora
This raises strong concerns on their generalizability -- a must-have for downstream applications where the magnitude of domains or event mentions is likely to exceed those found in a curated corpus.
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.
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.
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.
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.
Empowering Active Learning to Jointly Optimize System and User Demands
Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training.
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.
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
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.
Contextual information integration for stance detection via cross-attention
Stance detection deals with identifying an author's stance towards a target.
A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking
Automated fact-checking based on machine learning is a promising approach to identify false information distributed on the web.
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.
Triple-Encoders: Representations That Fire Together, Wire Together
Search-based dialog models typically re-encode the dialog history at every turn, incurring high cost.
From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical Resources
We examine the effect of lemmatization and POS typing on word embedding performance in a novel resource-based evaluation scenario, as well as on standard similarity benchmarks.
Neural Duplicate Question Detection without Labeled Training Data
We show that our proposed approaches are more effective in many cases because they can utilize larger amounts of unlabeled data from cQA forums.
How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation
We then probe embeddings in a multilingual setup with design choices that lie in a 'stable region', as we identify for English, and find that results on English do not transfer to other languages.
Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision
We propose a differentiable training-framework to create models which output faithful rationales on a sentence level, by solely applying supervision on the target task.
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.
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
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.
Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs
Reasoning is a fundamental component of language understanding.
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.
Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation
The predominant RL paradigm for summarisation learns a cross-input policy, which requires considerable time, data and parameter tuning due to the huge search spaces and the delayed rewards.
Coreference Reasoning in Machine Reading Comprehension
We propose a methodology for creating MRC datasets that better reflect the challenges of coreference reasoning and use it to create a sample evaluation set.
Exploring Metaphoric Paraphrase Generation
Metaphor generation is a difficult task, and has seen tremendous improvement with the advent of deep pretrained models.
A Neural Graph-based Local Coherence Model
Entity grids and entity graphs are two frameworks for modeling local coherence.
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.
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.
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.
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.
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.
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.
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.
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.
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?
