Search Results for author: Iryna Gurevych

Found 246 papers, 111 papers with code

Exploring Metaphoric Paraphrase Generation

1 code implementation CoNLL (EMNLP) 2021 Kevin Stowe, Nils Beck, Iryna Gurevych

Metaphor generation is a difficult task, and has seen tremendous improvement with the advent of deep pretrained models.

Paraphrase Generation

Event Coreference Data (Almost) for Free: Mining Hyperlinks from Online News

1 code implementation AKBC 2021 Michael Bugert, Iryna Gurevych

Cross-document event coreference resolution (CDCR) is the task of identifying which event mentions refer to the same events throughout a collection of documents.

Coreference Resolution Event Coreference Resolution

MetaQA: Combining Expert Agents for Multi-Skill Question Answering

1 code implementation3 Dec 2021 Haritz Puerto, Gözde Gül Şahin, Iryna Gurevych

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.

Question Answering

xGQA: Cross-Lingual Visual Question Answering

1 code implementation13 Sep 2021 Jonas Pfeiffer, Gregor Geigle, Aishwarya Kamath, Jan-Martin O. Steitz, Stefan Roth, Ivan Vulić, Iryna Gurevych

In this work, we address this gap and provide xGQA, a new multilingual evaluation benchmark for the visual question answering task.

Language Modelling Question Answering +2

TxT: Crossmodal End-to-End Learning with Transformers

no code implementations9 Sep 2021 Jan-Martin O. Steitz, Jonas Pfeiffer, Iryna Gurevych, Stefan Roth

Reasoning over multiple modalities, e. g. in Visual Question Answering (VQA), requires an alignment of semantic concepts across domains.

Question Answering Visual Question Answering

Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning

1 code implementation EMNLP 2021 Prasetya Ajie Utama, Nafise Sadat Moosavi, Victor Sanh, Iryna Gurevych

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.

Language Modelling

Ranking Scientific Papers Using Preference Learning

no code implementations2 Sep 2021 Nils Dycke, Edwin Simpson, Ilia Kuznetsov, Iryna Gurevych

To assist with this important task, we cast it as a paper ranking problem based on peer review texts and reviewer scores.

Decision Making Fairness

AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters

1 code implementation18 Aug 2021 Tilman Beck, Bela Bohlender, Christina Viehmann, Vincent Hane, Yanik Adamson, Jaber Khuri, Jonas Brossmann, Jonas Pfeiffer, Iryna Gurevych

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.

Few-Shot Learning Transfer Learning

Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation

no code implementations1 Jul 2021 Anne Lauscher, Henning Wachsmuth, Iryna Gurevych, Goran Glavaš

In this survey paper, we fill this gap by (1) proposing a pyramid of types of knowledge required in CA tasks, (2) analysing the state of the art with respect to the reliance and exploitation of these types of knowledge, for each of the for main research areas in CA, and (3) outlining and discussing directions for future research efforts in CA.

Common Sense Reasoning Natural Language Understanding

Annotation Curricula to Implicitly Train Non-Expert Annotators

1 code implementation4 Jun 2021 Ji-Ung Lee, Jan-Christoph Klie, Iryna Gurevych

Annotation studies often require annotators to familiarize themselves with the task, its annotation scheme, and the data domain.

Metaphor Generation with Conceptual Mappings

1 code implementation ACL 2021 Kevin Stowe, Tuhin Chakrabarty, Nanyun Peng, Smaranda Muresan, Iryna Gurevych

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

1 code implementation ACL 2021 Tilman Beck, Ji-Ung Lee, Christina Viehmann, Marcus Maurer, Oliver Quiring, Iryna Gurevych

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.

Opinion Mining Transfer Learning

Combating Temporal Drift in Crisis with Adapted Embeddings

no code implementations17 Apr 2021 Kevin Stowe, Iryna Gurevych

Language usage changes over time, and this can impact the effectiveness of NLP systems.

BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

1 code implementation17 Apr 2021 Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, Iryna Gurevych

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.

Argument Retrieval Biomedical Information Retrieval +10

Learning to Reason for Text Generation from Scientific Tables

1 code implementation16 Apr 2021 Nafise Sadat Moosavi, Andreas Rücklé, Dan Roth, Iryna Gurevych

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.

Data-to-Text Generation

What to Pre-Train on? Efficient Intermediate Task Selection

1 code implementation EMNLP 2021 Clifton Poth, Jonas Pfeiffer, Andreas Rücklé, Iryna Gurevych

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.

Question Answering Transfer Learning

Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval

1 code implementation22 Mar 2021 Gregor Geigle, Jonas Pfeiffer, Nils Reimers, Ivan Vulić, Iryna Gurevych

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.

Cross-Modal Retrieval

Structural Adapters in Pretrained Language Models for AMR-to-text Generation

1 code implementation EMNLP 2021 Leonardo F. R. Ribeiro, Yue Zhang, Iryna Gurevych

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.

AMR-to-Text Generation Text Generation

Focusing Knowledge-based Graph Argument Mining via Topic Modeling

no code implementations3 Feb 2021 Patrick Abels, Zahra Ahmadi, Sophie Burkhardt, Benjamin Schiller, Iryna Gurevych, Stefan Kramer

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.

Argument Mining Decision Making +1

Empirical Evaluation of Supervision Signals for Style Transfer Models

no code implementations15 Jan 2021 Yevgeniy Puzikov, Simoes Stanley, Iryna Gurevych, Immanuel Schweizer

In this work we empirically compare the dominant optimization paradigms which provide supervision signals during training: backtranslation, adversarial training and reinforcement learning.

Machine Translation Style Transfer +2

UNKs Everywhere: Adapting Multilingual Language Models to New Scripts

2 code implementations EMNLP 2021 Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, Sebastian Ruder

The ultimate challenge is dealing with under-resourced languages not covered at all by the models and written in scripts unseen during pretraining.

Cross-Lingual Transfer

Coreference Reasoning in Machine Reading Comprehension

1 code implementation ACL 2021 Mingzhu Wu, Nafise Sadat Moosavi, Dan Roth, Iryna Gurevych

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.

Coreference Resolution Machine Reading Comprehension +2

How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models

1 code implementation ACL 2021 Phillip Rust, Jonas Pfeiffer, Ivan Vulić, Sebastian Ruder, Iryna Gurevych

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.

The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes

no code implementations ACL 2021 Nils Reimers, Iryna Gurevych

Information Retrieval using dense low-dimensional representations recently became popular and showed out-performance to traditional sparse-representations like BM25.

Information Retrieval

Generalizing Cross-Document Event Coreference Resolution Across Multiple Corpora

1 code implementation CL (ACL) 2021 Michael Bugert, Nils Reimers, Iryna Gurevych

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.

Coreference Resolution Event Coreference Resolution

Ranking Creative Language Characteristics in Small Data Scenarios

no code implementations23 Oct 2020 Julia Siekiera, Marius Köppel, Edwin Simpson, Kevin Stowe, Iryna Gurevych, Stefan Kramer

We therefore adapt the DirectRanker to provide a new deep model for ranking creative language with small data.

Improving Robustness by Augmenting Training Sentences with Predicate-Argument Structures

no code implementations23 Oct 2020 Nafise Sadat Moosavi, Marcel de Boer, Prasetya Ajie Utama, Iryna Gurevych

Existing approaches to improve robustness against dataset biases mostly focus on changing the training objective so that models learn less from biased examples.

Data Augmentation

AdapterDrop: On the Efficiency of Adapters in Transformers

1 code implementation EMNLP 2021 Andreas Rücklé, Gregor Geigle, Max Glockner, Tilman Beck, Jonas Pfeiffer, Nils Reimers, Iryna Gurevych

Massively pre-trained transformer models are computationally expensive to fine-tune, slow for inference, and have large storage requirements.

Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision

1 code implementation Findings of the Association for Computational Linguistics 2020 Max Glockner, Ivan Habernal, Iryna Gurevych

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.

Decision Making

MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale

1 code implementation EMNLP 2020 Andreas Rücklé, Jonas Pfeiffer, Iryna Gurevych

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.

Answer Selection Community Question Answering +3

Towards Debiasing NLU Models from Unknown Biases

1 code implementation EMNLP 2020 Prasetya Ajie Utama, Nafise Sadat Moosavi, Iryna Gurevych

Recently proposed debiasing methods are shown to be effective in mitigating this tendency.

Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning

1 code implementation3 Aug 2020 Tristan Miller, Erik-Lân Do Dinh, Edwin Simpson, Iryna Gurevych

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.

AdapterHub: A Framework for Adapting Transformers

3 code implementations EMNLP 2020 Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, Iryna Gurevych

We propose AdapterHub, a framework that allows dynamic "stitching-in" of pre-trained adapters for different tasks and languages.

How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation

1 code implementation CONLL 2020 Steffen Eger, Johannes Daxenberger, Iryna Gurevych

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.

Sentence Embeddings

Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers

1 code implementation EMNLP (DeeLIO) 2020 Anne Lauscher, Olga Majewska, Leonardo F. R. Ribeiro, Iryna Gurevych, Nikolai Rozanov, Goran Glavaš

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.

Common Sense Reasoning

Empowering Active Learning to Jointly Optimize System and User Demands

1 code implementation ACL 2020 Ji-Ung Lee, Christian M. Meyer, Iryna Gurevych

Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training.

Active Learning

Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance

1 code implementation ACL 2020 Prasetya Ajie Utama, Nafise Sadat Moosavi, Iryna Gurevych

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.

Natural Language Understanding

AdapterFusion: Non-Destructive Task Composition for Transfer Learning

2 code implementations EACL 2021 Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, Iryna Gurevych

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.

Language Modelling Multi-Task Learning

Low Resource Multi-Task Sequence Tagging -- Revisiting Dynamic Conditional Random Fields

no code implementations1 May 2020 Jonas Pfeiffer, Edwin Simpson, Iryna Gurevych

We compare different models for low resource multi-task sequence tagging that leverage dependencies between label sequences for different tasks.

Multi-Task Learning

Aspect-Controlled Neural Argument Generation

1 code implementation NAACL 2021 Benjamin Schiller, Johannes Daxenberger, Iryna Gurevych

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.

Data Augmentation Language Modelling +1

A Matter of Framing: The Impact of Linguistic Formalism on Probing Results

no code implementations EMNLP 2020 Ilia Kuznetsov, Iryna Gurevych

Deep pre-trained contextualized encoders like BERT (Delvin et al., 2019) demonstrate remarkable performance on a range of downstream tasks.

MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer

3 code implementations EMNLP 2020 Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, Sebastian Ruder

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 #2 on Cross-Lingual Transfer on XCOPA (using extra training data)

Cross-Lingual Transfer Named Entity Recognition +1

Improving Factual Consistency Between a Response and Persona Facts

no code implementations EACL 2021 Mohsen Mesgar, Edwin Simpson, Iryna Gurevych

Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with facts describing the speaker's persona.

PuzzLing Machines: A Challenge on Learning From Small Data

no code implementations ACL 2020 Gözde Gül Şahin, Yova Kementchedjhieva, Phillip Rust, Iryna Gurevych

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.

Small Data Image Classification

Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

7 code implementations EMNLP 2020 Nils Reimers, Iryna Gurevych

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.

Knowledge Distillation Sentence Embedding

Metaphoric Paraphrase Generation

no code implementations28 Feb 2020 Kevin Stowe, Leonardo Ribeiro, Iryna Gurevych

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.

Paraphrase Generation

Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings

1 code implementation ICLR 2020 Shweta Mahajan, Iryna Gurevych, Stefan Roth

Therefore, we propose a novel semi-supervised framework, which models shared information between domains and domain-specific information separately.

Image Captioning Image Generation

Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs

1 code implementation29 Jan 2020 Leonardo F. R. Ribeiro, Yue Zhang, Claire Gardent, Iryna Gurevych

Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations.

Graph-to-Sequence KG-to-Text Generation +1

Stance Detection Benchmark: How Robust Is Your Stance Detection?

1 code implementation6 Jan 2020 Benjamin Schiller, Johannes Daxenberger, Iryna Gurevych

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.

Fake News Detection Multi-Task Learning +1

Analyzing Structures in the Semantic Vector Space: A Framework for Decomposing Word Embeddings

1 code implementation17 Dec 2019 Andreas Hanselowski, Iryna Gurevych

Word embeddings are rich word representations, which in combination with deep neural networks, lead to large performance gains for many NLP tasks.

Word Embeddings

Two Birds with One Stone: Investigating Invertible Neural Networks for Inverse Problems in Morphology

no code implementations11 Dec 2019 Gözde Gül Şahin, Iryna Gurevych

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).

Scalable Bayesian Preference Learning for Crowds

1 code implementation4 Dec 2019 Edwin Simpson, Iryna Gurevych

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.

Gaussian Processes Variational Inference

When is ACL's Deadline? A Scientific Conversational Agent

no code implementations23 Nov 2019 Mohsen Mesgar, Paul Youssef, Lin Li, Dominik Bierwirth, Yihao Li, Christian M. Meyer, Iryna Gurevych

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.

Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation

1 code implementation22 Nov 2019 Edwin Simpson, Yang Gao, Iryna Gurevych

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.

Bayesian Optimisation Community Question Answering +1

Neural Duplicate Question Detection without Labeled Training Data

1 code implementation IJCNLP 2019 Andreas Rücklé, Nafise Sadat Moosavi, Iryna Gurevych

We show that our proposed approaches are more effective in many cases because they can utilize larger amounts of unlabeled data from cQA forums.

Answer Selection Community Question Answering +1

Revisiting the Binary Linearization Technique for Surface Realization

no code implementations WS 2019 Yevgeniy Puzikov, Claire Gardent, Ido Dagan, Iryna Gurevych

End-to-end neural approaches have achieved state-of-the-art performance in many natural language processing (NLP) tasks.

Decision Making

Improving Generalization by Incorporating Coverage in Natural Language Inference

no code implementations19 Sep 2019 Nafise Sadat Moosavi, Prasetya Ajie Utama, Andreas Rücklé, Iryna Gurevych

Finally, we show that using the coverage information is not only beneficial for improving the performance across different datasets of the same task.

Natural Language Inference

Joint Wasserstein Autoencoders for Aligning Multimodal Embeddings

no code implementations14 Sep 2019 Shweta Mahajan, Teresa Botschen, Iryna Gurevych, Stefan Roth

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.

Cross-Modal Retrieval

What do Deep Networks Like to Read?

no code implementations10 Sep 2019 Jonas Pfeiffer, Aishwarya Kamath, Iryna Gurevych, Sebastian Ruder

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.

Better Rewards Yield Better Summaries: Learning to Summarise Without References

2 code implementations IJCNLP 2019 Florian Böhm, Yang Gao, Christian M. Meyer, Ori Shapira, Ido Dagan, Iryna Gurevych

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.

Enhancing AMR-to-Text Generation with Dual Graph Representations

1 code implementation IJCNLP 2019 Leonardo F. R. Ribeiro, Claire Gardent, Iryna Gurevych

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.

AMR-to-Text Generation Data-to-Text Generation +1

FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning

no code implementations IJCNLP 2019 Jonas Pfeiffer, Christian M. Meyer, Claudia Schulz, Jan Kiesewetter, Jan Zottmann, Michael Sailer, Elisabeth Bauer, Frank Fischer, Martin R. Fischer, Iryna Gurevych

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.

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

39 code implementations IJCNLP 2019 Nils Reimers, Iryna Gurevych

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.

Semantic Similarity Semantic Textual Similarity +2

Dialogue Coherence Assessment Without Explicit Dialogue Act Labels

1 code implementation ACL 2020 Mohsen Mesgar, Sebastian Bücker, Iryna Gurevych

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.

Multi-Task Learning

Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation

1 code implementation30 Jul 2019 Yang Gao, Christian M. Meyer, Mohsen Mesgar, Iryna Gurevych

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.

Decision Making Learning-To-Rank

Preference-based Interactive Multi-Document Summarisation

1 code implementation7 Jun 2019 Yang Gao, Christian M. Meyer, Iryna Gurevych

Interactive NLP is a promising paradigm to close the gap between automatic NLP systems and the human upper bound.

Active Learning

Pitfalls in the Evaluation of Sentence Embeddings

no code implementations WS 2019 Steffen Eger, Andreas Rücklé, Iryna Gurevych

Our motivation is to challenge the current evaluation of sentence embeddings and to provide an easy-to-access reference for future research.

Sentence Embeddings

Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization

1 code implementation NAACL 2019 Tobias Falke, Iryna Gurevych

Concept map-based multi-document summarization has recently been proposed as a variant of the traditional summarization task with graph-structured summaries.

Document Summarization Multi-Document Summarization

A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd

1 code implementation NAACL 2019 Tristan Miller, Maria Sukhareva, Iryna Gurevych

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.

Argument Mining

Alternative Weighting Schemes for ELMo Embeddings

1 code implementation5 Apr 2019 Nils Reimers, Iryna Gurevych

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.

Language Modelling Word Embeddings

Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems

no code implementations NAACL 2019 Steffen Eger, Gözde Gül Şahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych

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.

Adversarial Attack

Does My Rebuttal Matter? Insights from a Major NLP Conference

1 code implementation NAACL 2019 Yang Gao, Steffen Eger, Ilia Kuznetsov, Iryna Gurevych, Yusuke Miyao

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.

Predicting Research Trends From Arxiv

1 code implementation7 Mar 2019 Steffen Eger, Chao Li, Florian Netzer, Iryna Gurevych

By extrapolation, we predict that these topics will remain lead problems/approaches in their fields in the short- and mid-term.

Text Generation

Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks

1 code implementation EMNLP 2018 Steffen Eger, Paul Youssef, Iryna Gurevych

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.

Image Classification

Challenges in the Automatic Analysis of Students' Diagnostic Reasoning

1 code implementation26 Nov 2018 Claudia Schulz, Christian M. Meyer, Michael Sailer, Jan Kiesewetter, Elisabeth Bauer, Frank Fischer, Martin R. Fischer, Iryna Gurevych

We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification.

A Bayesian Approach for Sequence Tagging with Crowds

1 code implementation IJCNLP 2019 Edwin Simpson, Iryna Gurevych

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.

Active Learning Argument Mining +1

Frame- and Entity-Based Knowledge for Common-Sense Argumentative Reasoning

1 code implementation WS 2018 Teresa Botschen, Daniil Sorokin, Iryna Gurevych

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.

Argument Mining Common Sense Reasoning +7

Corpus-Driven Thematic Hierarchy Induction

no code implementations CONLL 2018 Ilia Kuznetsov, Iryna Gurevych

Thematic role hierarchy is a widely used linguistic tool to describe interactions between semantic roles and their syntactic realizations.

Machine Translation Question Answering +1

UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification

1 code implementation WS 2018 Andreas Hanselowski, Hao Zhang, Zile Li, Daniil Sorokin, Benjamin Schiller, Claudia Schulz, Iryna Gurevych

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.

Entity Linking General Classification +1

APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning

1 code implementation EMNLP 2018 Yang Gao, Christian M. Meyer, Iryna Gurevych

The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries.

Active Learning

One Size Fits All? A simple LSTM for non-literal token and construction-level classification

no code implementations COLING 2018 Erik-L{\^a}n Do Dinh, Steffen Eger, Iryna Gurevych

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.

General Classification Multi-Task Learning

The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation

no code implementations COLING 2018 Jan-Christoph Klie, Michael Bugert, Beto Boullosa, Richard Eckart de Castilho, Iryna Gurevych

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).

Active Learning Entity Linking +1

From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical Resources

1 code implementation COLING 2018 Ilia Kuznetsov, Iryna Gurevych

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.

Coreference Resolution Lemmatization +2

A Retrospective Analysis of the Fake News Challenge Stance-Detection Task

1 code implementation COLING 2018 Andreas Hanselowski, Avinesh PVS, Benjamin Schiller, Felix Caspelherr, Debanjan Chaudhuri, Christian M. Meyer, Iryna Gurevych

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.

General Classification Stance Classification +1

BinLin: A Simple Method of Dependency Tree Linearization

no code implementations WS 2018 Yevgeniy Puzikov, Iryna Gurevych

Surface Realization Shared Task 2018 is a workshop on generating sentences from lemmatized sets of dependency triples.

Text Generation

Multimodal Grounding for Language Processing

1 code implementation COLING 2018 Lisa Beinborn, Teresa Botschen, Iryna Gurevych

This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language.

A Retrospective Analysis of the Fake News Challenge Stance Detection Task

7 code implementations13 Jun 2018 Andreas Hanselowski, Avinesh PVS, Benjamin Schiller, Felix Caspelherr, Debanjan Chaudhuri, Christian M. Meyer, Iryna Gurevych

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.

General Classification Stance Classification +1

Finding Convincing Arguments Using Scalable Bayesian Preference Learning

1 code implementation TACL 2018 Edwin Simpson, Iryna Gurevych

We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard rat- ings or rankings.

Active Learning Variational Inference +1

A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning

no code implementations SEMEVAL 2018 Hatem Mousselly-Sergieh, Teresa Botschen, Iryna Gurevych, Stefan Roth

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.

Graph Representation Learning Information Retrieval +4

Objective Function Learning to Match Human Judgements for Optimization-Based Summarization

no code implementations NAACL 2018 Maxime Peyrard, Iryna Gurevych

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.

Multi-Task Learning for Argumentation Mining in Low-Resource Settings

1 code implementation NAACL 2018 Claudia Schulz, Steffen Eger, Johannes Daxenberger, Tobias Kahse, Iryna Gurevych

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.

Multi-Task Learning

Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approaches

1 code implementation26 Mar 2018 Nils Reimers, Iryna Gurevych

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.

NER

Event Time Extraction with a Decision Tree of Neural Classifiers

no code implementations TACL 2018 Nils Reimers, Nazanin Dehghani, Iryna Gurevych

We use this tree to incrementally infer, in a stepwise manner, at which time frame an event happened.

Lexical-semantic resources: yet powerful resources for automatic personality classification

no code implementations GWC 2018 Xuan-Son Vu, Lucie Flekova, Lili Jiang, Iryna Gurevych

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.

General Classification Word Sense Disambiguation

Real-Time News Summarization with Adaptation to Media Attention

no code implementations RANLP 2017 Andreas R{\"u}ckl{\'e}, Iryna Gurevych

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.

Decision Making

Context-Aware Representations for Knowledge Base Relation Extraction

1 code implementation EMNLP 2017 Daniil Sorokin, Iryna Gurevych

We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation.

Question Answering Relation Extraction

GraphDocExplore: A Framework for the Experimental Comparison of Graph-based Document Exploration Techniques

no code implementations EMNLP 2017 Tobias Falke, Iryna Gurevych

Many techniques to automatically extract different types of graphs, showing for example entities or concepts and different relationships between them, have been suggested.

SemEval-2017 Task 7: Detection and Interpretation of English Puns

no code implementations SEMEVAL 2017 Tristan Miller, Christian Hempelmann, Iryna Gurevych

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.

Word Sense Disambiguation

Prediction of Frame-to-Frame Relations in the FrameNet Hierarchy with Frame Embeddings

no code implementations WS 2017 Teresa Botschen, Hatem Mousselly-Sergieh, Iryna Gurevych

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.

Natural Language Inference Representation Learning +1

Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging

5 code implementations EMNLP 2017 Nils Reimers, Iryna Gurevych

In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches.

NER

Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks

5 code implementations21 Jul 2017 Nils Reimers, Iryna Gurevych

Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance.

Chunking Event Detection +4

Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets

no code implementations ACL 2017 Gabriel Stanovsky, Judith Eckle-Kohler, Yevgeniy Puzikov, Ido Dagan, Iryna Gurevych

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.

Knowledge Base Population Question Answering

Neural End-to-End Learning for Computational Argumentation Mining

2 code implementations ACL 2017 Steffen Eger, Johannes Daxenberger, Iryna Gurevych

Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results.

Dependency Parsing General Classification +1

EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION

1 code implementation SEMEVAL 2017 Steffen Eger, Erik-Lân Do Dinh, Ilia Kuznetsov, Masoud Kiaeeha, Iryna Gurevych

From these approaches, we created an ensemble of differently hyper-parameterized systems, achieving a micro-F1-score of 0. 63 on the test data.

General Classification

A Consolidated Open Knowledge Representation for Multiple Texts

1 code implementation WS 2017 Rachel Wities, Vered Shwartz, Gabriel Stanovsky, Meni Adler, Ori Shapira, Shyam Upadhyay, Dan Roth, Eugenio Martinez Camara, Iryna Gurevych, Ido Dagan

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.

Lexical Entailment Open Information Extraction

Metaheuristic Approaches to Lexical Substitution and Simplification

no code implementations EACL 2017 Sallam Abualhaija, Tristan Miller, Judith Eckle-Kohler, Iryna Gurevych, Karl-Heinz Zimmermann

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.

Lexical Simplification Machine Translation +4

A tool for extracting sense-disambiguated example sentences through user feedback

no code implementations EACL 2017 Beto Boullosa, Richard Eckart de Castilho, Alex Geyken, er, Lothar Lemnitzer, Iryna Gurevych

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.

General Classification

Out-of-domain FrameNet Semantic Role Labeling

no code implementations EACL 2017 Silvana Hartmann, Ilia Kuznetsov, Teresa Martin, Iryna Gurevych

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.

Semantic Role Labeling