Search Results for author:
Found 328 papers, 173 papers with code
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).
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.
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.
Predicting the Difficulty of Language Proficiency Tests
Language proficiency tests are used to evaluate and compare the progress of language learners.
Lexical Substitution Dataset for German
This article describes a lexical substitution dataset for German.
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.
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 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.
Parsing Argumentation Structures in Persuasive Essays
In this article, we present a novel approach for parsing argumentation structures.
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.
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.
C4Corpus: Multilingual Web-size Corpus with Free License
Large Web corpora containing full documents with permissive licenses are crucial for many NLP tasks.
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.
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.
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.
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
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.
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.
A domain-agnostic approach for opinion prediction on speech
We explore a domain-agnostic approach for analyzing speech with the goal of opinion prediction.
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.
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.
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.
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.
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.
Recognizing Insufficiently Supported Arguments in Argumentative Essays
In this paper, we propose a new task for assessing the quality of natural language arguments.
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.
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.
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.
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.
What is the Essence of a Claim? Cross-Domain Claim Identification
Argument mining has become a popular research area in NLP.
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.
Argumentation Quality Assessment: Theory vs. Practice
Argumentation quality is viewed differently in argumentation theory and in practical assessment approaches.
Argotario: Computational Argumentation Meets Serious Games
An important skill in critical thinking and argumentation is the ability to spot and recognize fallacies.
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.
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.
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.
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.
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.
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.
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
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.
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.
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?
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.
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.
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.
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.
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.
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.
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.
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.
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
ArgumenText: Searching for Arguments in Heterogeneous Sources
Argument mining is a core technology for enabling argument search in large corpora.
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.
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.
Multimodal Frame Identification with Multilingual Evaluation
Our analysis shows that for the German data, textual representations are still competitive with multimodal ones.
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.
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.
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.
Multimodal Grounding for Language Processing
This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language.
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.
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.
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.
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).
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.
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.
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.
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
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.
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.
Corpus-Driven Thematic Hierarchy Induction
Thematic role hierarchy is a widely used linguistic tool to describe interactions between semantic roles and their syntactic realizations.
Cross-topic Argument Mining from Heterogeneous Sources
Argument mining is a core technology for automating argument search in large document collections.
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
Interactive Instance-based Evaluation of Knowledge Base Question Answering
Most approaches to Knowledge Base Question Answering are based on semantic parsing.
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.
Cross-Lingual Argumentative Relation Identification: from English to Portuguese
Argument mining aims to detect and identify argument structures from textual resources.
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.
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.
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.
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.
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.
LINSPECTOR: Multilingual Probing Tasks for Word Representations
We present a reusable methodology for creation and evaluation of such tests in a multilingual setting.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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)
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.
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.
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.
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.
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.
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.
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
