Search Results for author: er

Found 339 papers, 24 papers with code

Modeling Word Formation in English--German Neural Machine Translation

no code implementations ACL 2020 Marion Weller-Di Marco, Alex Fraser, er

This paper studies strategies to model word formation in NMT using rich linguistic information, namely a word segmentation approach that goes beyond splitting into substrings by considering fusional morphology.

Machine Translation Morphological Analysis

Frugal Paradigm Completion

no code implementations ACL 2020 Alex Erdmann, er, Tom Kenter, Markus Becker, Christian Schallhart

Lexica distinguishing all morphologically related forms of each lexeme are crucial to many language technologies, yet building them is expensive.

Testing the role of metadata in metaphor identification

no code implementations WS 2020 Egon Stemle, Alex Onysko, er

The particular focus of our approach is on the potential influence that the metadata given in the ETS Corpus of Non-Native Written English might have on the automatic detection of metaphors in this dataset.

Torch-Struct: Deep Structured Prediction Library

1 code implementation ACL 2020 Alex Rush, er

The literature on structured prediction for NLP describes a rich collection of distributions and algorithms over sequences, segmentations, alignments, and trees; however, these algorithms are difficult to utilize in deep learning frameworks.

Structured Prediction

Towards Stream Translation: Adaptive Computation Time for Simultaneous Machine Translation

no code implementations WS 2020 Felix Schneider, Alex Waibel, er

Simultaneous machine translation systems rely on a policy to schedule read and write operations in order to begin translating a source sentence before it is complete.

automatic-speech-recognition Machine Translation +1

Metaphor Detection Using Contextual Word Embeddings From Transformers

no code implementations WS 2020 Jerry Liu, Nathan O{'}Hara, Alex Rubin, er, Rachel Draelos, Cynthia Rudin

The detection of metaphors can provide valuable information about a given text and is crucial to sentiment analysis and machine translation.

Machine Translation Sentiment Analysis +1

FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN

no code implementations WS 2020 Ebrahim Ansari, Amittai Axelrod, Nguyen Bach, Ond{\v{r}}ej Bojar, Roldano Cattoni, Fahim Dalvi, Nadir Durrani, Marcello Federico, Christian Federmann, Jiatao Gu, Fei Huang, Kevin Knight, Xutai Ma, Ajay Nagesh, Matteo Negri, Jan Niehues, Juan Pino, Elizabeth Salesky, Xing Shi, Sebastian St{\"u}ker, Marco Turchi, Alex Waibel, er, Changhan Wang

The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2020) featured this year six challenge tracks: (i) Simultaneous speech translation, (ii) Video speech translation, (iii) Offline speech translation, (iv) Conversational speech translation, (v) Open domain translation, and (vi) Non-native speech translation.

Beyond lexical semantics: notes on pragmatic frames

no code implementations LREC 2020 Oliver Czulo, Alex Ziem, er, Tiago Timponi Torrent

Framenets as an incarnation of frame semantics have been set up to deal with lexicographic issues (cf.

Voting for POS tagging of Latin texts: Using the flair of FLAIR to better Ensemble Classifiers by Example of Latin

no code implementations LREC 2020 Manuel Stoeckel, Alex Henlein, Wahed Hemati, Alex Mehler, er

Since most of the available Latin word embeddings were trained on either few or inaccurate data, we trained several embeddings on better data in the first step.

Lemmatization Part-Of-Speech Tagging +2

Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet

no code implementations LREC 2020 Christos Rodosthenous, Verena Lyding, Federico Sangati, Alex K{\"o}nig, er, Umair ul Hassan, Lionel Nicolas, Jolita Horbacauskiene, Anisia Katinskaia, Lavinia Aparaschivei

In this work, we report on a crowdsourcing experiment conducted using the V-TREL vocabulary trainer which is accessed via a Telegram chatbot interface to gather knowledge on word relations suitable for expanding ConceptNet.

Chatbot

Reconstructing NER Corpora: a Case Study on Bulgarian

no code implementations LREC 2020 Iva Marinova, Laska Laskova, Petya Osenova, Kiril Simov, Alex Popov, er

The paper reports on the usage of deep learning methods for improving a Named Entity Recognition (NER) training corpus and for predicting and annotating new types in a test corpus.

Named Entity Recognition NER

TheRuSLan: Database of Russian Sign Language

no code implementations LREC 2020 Ildar Kagirov, Denis Ivanko, Dmitry Ryumin, Alex Axyonov, er, Alexey Karpov

The database includes lexical units (single words and phrases) from Russian sign language within one subject area, namely, {``}food products at the supermarket{''}, and was collected using MS Kinect 2. 0 device including both FullHD video and the depth map modes, which provides new opportunities for the lexicographical description of the Russian sign language vocabulary and enhances research in the field of automatic gesture recognition.

Gesture Recognition Sign Language Recognition

Open-source Multi-speaker Corpora of the English Accents in the British Isles

no code implementations LREC 2020 Isin Demirsahin, Oddur Kjartansson, Alex Gutkin, er, Clara Rivera

This paper presents a dataset of transcribed high-quality audio of English sentences recorded by volunteers speaking with different accents of the British Isles.

Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems

no code implementations LREC 2020 Fei He, Shan-Hui Cathy Chu, Oddur Kjartansson, Clara Rivera, Anna Katanova, Alex Gutkin, er, Isin Demirsahin, Cibu Johny, Martin Jansche, Supheakmungkol Sarin, Knot Pipatsrisawat

We present free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu, which are six of the twenty two official languages of India spoken by 374 million native speakers.

Speech Synthesis

Burmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application to Text-to-Speech

no code implementations LREC 2020 Yin May Oo, Theeraphol Wattanavekin, Chenfang Li, Pasindu De Silva, Supheakmungkol Sarin, Knot Pipatsrisawat, Martin Jansche, Oddur Kjartansson, Alex Gutkin, er

This paper introduces an open-source crowd-sourced multi-speaker speech corpus along with the comprehensive set of finite-state transducer (FST) grammars for performing text normalization for the Burmese (Myanmar) language.

Exploring Bilingual Word Embeddings for Hiligaynon, a Low-Resource Language

no code implementations LREC 2020 Leah Michel, Viktor Hangya, Alex Fraser, er

We use a publicly available Hiligaynon corpus with only 300K words, and match it with a comparable corpus in English.

Word Embeddings

LMU Bilingual Dictionary Induction System with Word Surface Similarity Scores for BUCC 2020

no code implementations LREC 2020 Silvia Severini, Viktor Hangya, Alex Fraser, er, Hinrich Sch{\"u}tze

We participate in both the open and closed tracks of the shared task and we show improved results of our method compared to simple vector similarity based approaches.

Machine Translation Word Embeddings +1

Discovering Biased News Articles Leveraging Multiple Human Annotations

no code implementations LREC 2020 Konstantina Lazaridou, Alex L{\"o}ser, er, Maria Mestre, Felix Naumann

Yet, political propaganda and one-sided views can be found in the news and can cause distrust in media.

Transfer of ISOSpace into a 3D Environment for Annotations and Applications

no code implementations LREC 2020 Alex Henlein, Giuseppe Abrami, Attila Kett, Alex Mehler, er

People{'}s visual perception is very pronounced and therefore it is usually no problem for them to describe the space around them in words.

Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech

no code implementations LREC 2020 Adriana Guevara-Rukoz, Isin Demirsahin, Fei He, Shan-Hui Cathy Chu, Supheakmungkol Sarin, Knot Pipatsrisawat, Alex Gutkin, er, Alena Butryna, Oddur Kjartansson

In this paper we present a multidialectal corpus approach for building a text-to-speech voice for a new dialect in a language with existing resources, focusing on various South American dialects of Spanish.

Digital Language Infrastructures -- Documenting Language Actors

no code implementations LREC 2020 Verena Lyding, Alex K{\"o}nig, er, Monica Pretti

The major European language infrastructure initiatives like CLARIN (Hinrichs and Krauwer, 2014), DARIAH (Edmond et al., 2017) or Europeana (Europeana Foundation, 2015) have been built by focusing in the first place on institutions of larger scale, like specialized research departments and larger official units like national libraries, etc.

TextAnnotator: A UIMA Based Tool for the Simultaneous and Collaborative Annotation of Texts

no code implementations LREC 2020 Giuseppe Abrami, Manuel Stoeckel, Alex Mehler, er

The annotation of texts and other material in the field of digital humanities and Natural Language Processing (NLP) is a common task of research projects.

Developing a Corpus of Indirect Speech Act Schemas

no code implementations LREC 2020 Antonio Roque, Alex Tsuetaki, er, Vasanth Sarathy, Matthias Scheutz

Resolving Indirect Speech Acts (ISAs), in which the intended meaning of an utterance is not identical to its literal meaning, is essential to enabling the participation of intelligent systems in peoples{'} everyday lives.

Eidos: An Open-Source Auditory Periphery Modeling Toolkit and Evaluation of Cross-Lingual Phonemic Contrasts

no code implementations LREC 2020 Alex Gutkin, er

While the auditory periphery mechanisms responsible for transducing the sound pressure wave into the auditory nerve discharge are relatively well understood, the models that describe them are usually very complex because they try to faithfully simulate the behavior of several functionally distinct biological units involved in hearing.

Modelling Frequency and Attestations for OntoLex-Lemon

no code implementations LREC 2020 Christian Chiarcos, Maxim Ionov, Jesse de Does, Katrien Depuydt, Anas Fahad Khan, S Stolk, er, Thierry Declerck, John Philip McCrae

Therefore, the OntoLex community has put forward the proposal for a novel module for frequency, attestation and corpus information (FrAC), that not only covers the requirements of digital lexicography, but also accommodates essential data structures for lexical information in natural language processing.

Consistent Unsupervised Estimators for Anchored PCFGs

no code implementations TACL 2020 Alex Clark, er, Nathana{\"e}l Fijalkow

Learning probabilistic context-free grammars (PCFGs) from strings is a classic problem in computational linguistics since Horning (1969).

Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims?

no code implementations WS 2019 Aleks Wawer, er, Grzegorz Wojdyga, Justyna Sarzy{\'n}ska-Wawer

The goal of our paper is to compare psycholinguistic text features with fact checking approaches to distinguish lies from true statements.

Deception Detection Fact Checking

Saarland at MRP 2019: Compositional parsing across all graphbanks

no code implementations CONLL 2019 Lucia Donatelli, Meaghan Fowlie, Jonas Groschwitz, Alex Koller, er, Matthias Lindemann, Mario Mina, Pia Wei{\ss}enhorn

We describe the Saarland University submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference on Computational Natural Language Learning (CoNLL).

BIOfid Dataset: Publishing a German Gold Standard for Named Entity Recognition in Historical Biodiversity Literature

no code implementations CONLL 2019 Sajawel Ahmed, Manuel Stoeckel, Christine Driller, Adrian Pachzelt, Alex Mehler, er

The Specialized Information Service Biodiversity Research (BIOfid) has been launched to mobilize valuable biological data from printed literature hidden in German libraries for over the past 250 years.

Named Entity Recognition NER +1

Talking about what is not there: Generating indefinite referring expressions in Minecraft

no code implementations WS 2019 Arne K{\"o}hn, Alex Koller, er

When generating technical instructions, it is often necessary to describe an object that does not exist yet.

Minecraft

A Personalized Data-to-Text Support Tool for Cancer Patients

no code implementations WS 2019 Saar Hommes, Chris van der Lee, Felix Clouth, Jeroen Vermunt, X Verbeek, er, Emiel Krahmer

In this paper, we present a novel data-to-text system for cancer patients, providing information on quality of life implications after treatment, which can be embedded in the context of shared decision making.

Decision Making

Graph Embeddings for Frame Identification

no code implementations RANLP 2019 Alex Popov, er, Jennifer Sikos

Lexical resources such as WordNet (Miller, 1995) and FrameNet (Baker et al., 1998) are organized as graphs, where relationships between words are made explicit via the structure of the resource.

v-trel: Vocabulary Trainer for Tracing Word Relations - An Implicit Crowdsourcing Approach

no code implementations RANLP 2019 Verena Lyding, Christos Rodosthenous, Federico Sangati, Umair ul Hassan, Lionel Nicolas, Alex K{\"o}nig, er, Jolita Horbacauskiene, Anisia Katinskaia

In this paper, we present our work on developing a vocabulary trainer that uses exercises generated from language resources such as ConceptNet and crowdsources the responses of the learners to enrich the language resource.

Know Your Graph. State-of-the-Art Knowledge-Based WSD

no code implementations RANLP 2019 Alex Popov, er, Kiril Simov, Petya Osenova

This paper introduces several improvements over the current state of the art in knowledge-based word sense disambiguation.

Word Sense Disambiguation

Predicting Sentiment of Polish Language Short Texts

no code implementations RANLP 2019 Aleks Wawer, er, Julita Sobiczewska

In the second we train models on all available data except the given test collection, which we use for testing (one vs rest cross-domain).

Sentiment Analysis

Combining Lexical Substitutes in Neural Word Sense Induction

no code implementations RANLP 2019 Nikolay Arefyev, Boris Sheludko, Alex Panchenko, er

Word Sense Induction (WSI) is the task of grouping of occurrences of an ambiguous word according to their meaning.

Word Sense Induction

The LMU Munich Unsupervised Machine Translation System for WMT19

no code implementations WS 2019 Dario Stojanovski, Viktor Hangya, Matthias Huck, Alex Fraser, er

We describe LMU Munich{'}s machine translation system for German→Czech translation which was used to participate in the WMT19 shared task on unsupervised news translation.

Denoising Language Modelling +2

A Little Linguistics Goes a Long Way: Unsupervised Segmentation with Limited Language Specific Guidance

no code implementations WS 2019 Alex Erdmann, er, Salam Khalifa, Mai Oudah, Nizar Habash, Houda Bouamor

We present de-lexical segmentation, a linguistically motivated alternative to greedy or other unsupervised methods, requiring only minimal language specific input.

The Meaning of ``Most'' for Visual Question Answering Models

no code implementations WS 2019 Alex Kuhnle, er, Ann Copestake

The correct interpretation of quantifier statements in the context of a visual scene requires non-trivial inference mechanisms.

Question Answering Visual Question Answering

Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings

no code implementations WS 2019 Segun Taofeek Aroyehun, Alex Gelbukh, er

This paper details our approach to the task of detecting reportage of adverse drug reaction in tweets as part of the 2019 social media mining for healthcare applications shared task.

Word Embeddings

A Dataset for Noun Compositionality Detection for a Slavic Language

1 code implementation WS 2019 Dmitry Puzyrev, Artem Shelmanov, Alex Panchenko, er, Ekaterina Artemova

This paper presents the first gold-standard resource for Russian annotated with compositionality information of noun compounds.

PROMT Systems for WMT 2019 Shared Translation Task

no code implementations WS 2019 Alex Molchanov, er

This paper describes the PROMT submissions for the WMT 2019 Shared News Translation Task.

Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19

no code implementations WS 2019 Dario Stojanovski, Alex Fraser, er

We describe LMU Munich{'}s machine translation system for English→German translation which was used to participate in the WMT19 shared task on supervised news translation.

Document-level Machine Translation

On the Compositionality Prediction of Noun Phrases using Poincar\'e Embeddings

no code implementations ACL 2019 Abhik Jana, Dima Puzyrev, Alex Panchenko, er, Pawan Goyal, Chris Biemann, Animesh Mukherjee

In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincar{\'e} embeddings in addition to the distributional information to detect compositionality for noun phrases.

Better OOV Translation with Bilingual Terminology Mining

no code implementations ACL 2019 Matthias Huck, Viktor Hangya, Alex Fraser, er

In our experiments we use a system trained on Europarl and mine sentences containing medical terms from monolingual data.

Machine Translation Word Embeddings

Large-Scale Transfer Learning for Natural Language Generation

1 code implementation ACL 2019 Sergey Golovanov, Rauf Kurbanov, Sergey Nikolenko, Kyryl Truskovskyi, Alex Tselousov, er, Thomas Wolf

Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks.

Open-Domain Dialog Text Generation +1

Graph-Based Meaning Representations: Design and Processing

1 code implementation ACL 2019 Alex Koller, er, Stephan Oepen, Weiwei Sun

This tutorial is on representing and processing sentence meaning in the form of labeled directed graphs.

TARGER: Neural Argument Mining at Your Fingertips

1 code implementation ACL 2019 Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alex Bondarenko, Matthias Hagen, Chris Biemann, Alex Panchenko, er

We present TARGER, an open source neural argument mining framework for tagging arguments in free input texts and for keyword-based retrieval of arguments from an argument-tagged web-scale corpus.

Argument Mining

Improving Neural Entity Disambiguation with Graph Embeddings

no code implementations ACL 2019 {\"O}zge Sevgili, Alex Panchenko, er, Chris Biemann

Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base.

Entity Disambiguation

Cross-lingual Annotation Projection Is Effective for Neural Part-of-Speech Tagging

no code implementations WS 2019 Matthias Huck, Diana Dutka, Alex Fraser, er

We tackle the important task of part-of-speech tagging using a neural model in the zero-resource scenario, where we have no access to gold-standard POS training data.

Part-Of-Speech Tagging POS

Scalable Methods for Annotating Legal-Decision Corpora

no code implementations WS 2019 Lisa Ferro, John Aberdeen, Karl Branting, Craig Pfeifer, Alex Yeh, er, Amartya Chakraborty

Recent research has demonstrated that judicial and administrative decisions can be predicted by machine-learning models trained on prior decisions.

Enriching the WebNLG corpus

1 code implementation WS 2018 Thiago Castro Ferreira, Diego Moussallem, Emiel Krahmer, S Wubben, er

This paper describes the enrichment of WebNLG corpus (Gardent et al., 2017a, b), with the aim to further extend its usefulness as a resource for evaluating common NLG tasks, including Discourse Ordering, Lexicalization and Referring Expression Generation.

Machine Translation Referring expression generation +1

Sentence Packaging in Text Generation from Semantic Graphs as a Community Detection Problem

no code implementations WS 2018 Alex Shvets, er, Simon Mille, Leo Wanner

An increasing amount of research tackles the challenge of text generation from abstract ontological or semantic structures, which are in their very nature potentially large connected graphs.

Community Detection Text Generation +1

Debugging Sequence-to-Sequence Models with Seq2Seq-Vis

no code implementations WS 2018 Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, Alex Rush, er

Neural attention-based sequence-to-sequence models (seq2seq) (Sutskever et al., 2014; Bahdanau et al., 2014) have proven to be accurate and robust for many sequence prediction tasks.

An Unsupervised System for Parallel Corpus Filtering

no code implementations WS 2018 Viktor Hangya, Alex Fraser, er

In this paper we describe LMU Munich{'}s submission for the \textit{WMT 2018 Parallel Corpus Filtering} shared task which addresses the problem of cleaning noisy parallel corpora.

Domain Adaptation Language Modelling +4

PROMT Systems for WMT 2018 Shared Translation Task

no code implementations WS 2018 Alex Molchanov, er

This paper describes the PROMT submissions for the WMT 2018 Shared News Translation Task.

Machine Translation

Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments

no code implementations WS 2018 Dario Stojanovski, Alex Fraser, er

We show that NMT models taking advantage of context oracle signals can achieve considerable gains in BLEU, of up to 7. 02 BLEU for coreference and 1. 89 BLEU for coherence on subtitles translation.

Coreference Resolution Language Modelling +1

Automatic Identification of Drugs and Adverse Drug Reaction Related Tweets

no code implementations WS 2018 Segun Taofeek Aroyehun, Alex Gelbukh, er

We describe our submissions to the Third Social Media Mining for Health Applications Shared Task.

LMU Munich's Neural Machine Translation Systems at WMT 2018

no code implementations WS 2018 Matthias Huck, Dario Stojanovski, Viktor Hangya, Alex Fraser, er

The systems were used for our participation in the WMT18 biomedical translation task and in the shared task on machine translation of news.

Domain Adaptation Unsupervised Machine Translation

LTV: Labeled Topic Vector

no code implementations COLING 2018 Daniel Baumartz, Tolga Uslu, Alex Mehler, er

In this paper we present LTV, a website and API that generates labeled topic classifications based on the Dewey Decimal Classification (DDC), an international standard for topic classification in libraries.

General Classification Semantic Textual Similarity +1

Evaluating the text quality, human likeness and tailoring component of PASS: A Dutch data-to-text system for soccer

no code implementations COLING 2018 Chris van der Lee, Bart Verduijn, Emiel Krahmer, S Wubben, er

We present an evaluation of PASS, a data-to-text system that generates Dutch soccer reports from match statistics which are automatically tailored towards fans of one club or the other.

Text Generation

Addressing Noise in Multidialectal Word Embeddings

no code implementations ACL 2018 Alex Erdmann, er, Nasser Zalmout, Nizar Habash

Arabic dialects lack large corpora and are noisy, being linguistically disparate with no standardized spelling.

Transliteration Word Embeddings

The Annotated Transformer

1 code implementation WS 2018 Alex Rush, er

A major goal of open-source NLP is to quickly and accurately reproduce the results of new work, in a manner that the community can easily use and modify.

Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable

1 code implementation ACL 2018 Viktor Hangya, Fabienne Braune, Alex Fraser, er, Hinrich Sch{\"u}tze

Bilingual tasks, such as bilingual lexicon induction and cross-lingual classification, are crucial for overcoming data sparsity in the target language.

Bilingual Lexicon Induction Classification +6

Surface Realization Shared Task 2018 (SR18): The Tilburg University Approach

1 code implementation WS 2018 Thiago Castro Ferreira, S Wubben, er, Emiel Krahmer

This study describes the approach developed by the Tilburg University team to the shallow task of the Multilingual Surface Realization Shared Task 2018 (SR18).

Machine Translation

Discourse Coherence: Concurrent Explicit and Implicit Relations

no code implementations ACL 2018 Hannah Rohde, Alex Johnson, er, Nathan Schneider, Bonnie Webber

Theories of discourse coherence posit relations between discourse segments as a key feature of coherent text.

Discourse Parsing

PunFields at SemEval-2018 Task 3: Detecting Irony by Tools of Humor Analysis

no code implementations SEMEVAL 2018 Elena Mikhalkova, Yuri Karyakin, Alex Voronov, er, Dmitry Grigoriev, Artem Leoznov

The paper describes our search for a universal algorithm of detecting intentional lexical ambiguity in different forms of creative language.

UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?

no code implementations SEMEVAL 2018 Alex Zhang, er, Marine Carpuat

We describe the University of Maryland{'}s submission to SemEval-018 Task 10, {``}Capturing Discriminative Attributes{''}: given word triples (w1, w2, d), the goal is to determine whether d is a discriminating attribute belonging to w1 but not w2.

Semantic Textual Similarity Word Embeddings

ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings

no code implementations SEMEVAL 2018 Lena Hettinger, Alex Dallmann, er, Albin Zehe, Thomas Niebler, Andreas Hotho

In this paper we describe our system for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1. 1) and noisy data (subtask 1. 2).

Classification General Classification +4

Using Language Learner Data for Metaphor Detection

1 code implementation WS 2018 Egon Stemle, Alex Onysko, er

This article describes the system that participated in the shared task on metaphor detection on the Vrije University Amsterdam Metaphor Corpus (VUA).

Language Identification Word Embeddings

Noise-Robust Morphological Disambiguation for Dialectal Arabic

no code implementations NAACL 2018 Nasser Zalmout, Alex Erdmann, er, Nizar Habash

User-generated text tends to be noisy with many lexical and orthographic inconsistencies, making natural language processing (NLP) tasks more challenging.

Lexical Normalization Morphological Analysis +3

Evaluating bilingual word embeddings on the long tail

1 code implementation NAACL 2018 Fabienne Braune, Viktor Hangya, Tobias Eder, Alex Fraser, er

Bilingual word embeddings are useful for bilingual lexicon induction, the task of mining translations of given words.

Bilingual Lexicon Induction Word Embeddings

NITMZ-JU at IJCNLP-2017 Task 4: Customer Feedback Analysis

no code implementations IJCNLP 2017 Somnath Banerjee, Partha Pakray, Riyanka Manna, Dipankar Das, Alex Gelbukh, er

In this paper, we describe a deep learning framework for analyzing the customer feedback as part of our participation in the shared task on Customer Feedback Analysis at the 8th International Joint Conference on Natural Language Processing (IJCNLP 2017).

Text Classification

Event Ordering with a Generalized Model for Sieve Prediction Ranking

no code implementations IJCNLP 2017 Bill McDowell, Nathanael Chambers, Alex Ororbia II, er, David Reitter

Within this prediction reranking framework, we propose an alternative scoring function, showing an 8. 8{\%} relative gain over the original CAEVO.

Word Embeddings

Integrated sentence generation using charts

no code implementations WS 2017 Alex Koller, er, Nikos Engonopoulos

Integrating surface realization and the generation of referring expressions into a single algorithm can improve the quality of the generated sentences.

Text Generation

Coarse-To-Fine Parsing for Expressive Grammar Formalisms

no code implementations WS 2017 Christoph Teichmann, Alex Koller, er, Jonas Groschwitz

We generalize coarse-to-fine parsing to grammar formalisms that are more expressive than PCFGs and/or describe languages of trees or graphs.

Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation

no code implementations WS 2017 Thiago Castro Ferreira, Iacer Calixto, S Wubben, er, Emiel Krahmer

In this paper, we study AMR-to-text generation, framing it as a translation task and comparing two different MT approaches (Phrase-based and Neural MT).

AMR-to-Text Generation Machine Translation +1

Word Sense Disambiguation with Recurrent Neural Networks

no code implementations RANLP 2017 Alex Popov, er

This paper presents a neural network architecture for word sense disambiguation (WSD).

Word Sense Disambiguation