Search Results for author: Sara Stymne

Found 38 papers, 4 papers with code

Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary?

no code implementations SemEval (NAACL) 2022 Rafal Cerniavski, Sara Stymne

In an additional experiment, using resources beyond the shared task, we use the training data in Russian and French to improve the English reverse dictionary using unsupervised embeddings alignment and machine translation.

Machine Translation Reverse Dictionary +2

A Mention-Based System for Revision Requirements Detection

no code implementations ACL (unimplicit) 2021 Ahmed Ruby, Christian Hardmeier, Sara Stymne

Exploring aspects of sentential meaning that are implicit or underspecified in context is important for sentence understanding.

Whit’s the Richt Pairt o Speech: PoS tagging for Scots

no code implementations EACL (VarDial) 2021 Harm Lameris, Sara Stymne

We find that training on a very small amount of Scots data was superior to zero-shot transfer from English.

POS Transfer Learning

IESTAC: English-Italian Parallel Corpus for End-to-End Speech-to-Text Machine Translation

1 code implementation EMNLP (nlpbt) 2020 Giuseppe Della Corte, Sara Stymne

We discuss a set of methods for the creation of IESTAC: a English-Italian speech and text parallel corpus designed for the training of end-to-end speech-to-text machine translation models and publicly released as part of this work.

Dynamic Time Warping Machine Translation +2

Uppsala NLP at SemEval-2021 Task 2: Multilingual Language Models for Fine-tuning and Feature Extraction in Word-in-Context Disambiguation

no code implementations SEMEVAL 2021 Huiling You, Xingran Zhu, Sara Stymne

XLMR performs better than mBERT in the cross-lingual setting both with fine-tuning and feature extraction, whereas these two models give a similar performance in the multilingual setting.

Evaluating Word Embeddings for Indonesian--English Code-Mixed Text Based on Synthetic Data

no code implementations LREC 2020 Arra{'}Di Nur Rizal, Sara Stymne

Code-mixed texts are abundant, especially in social media, and poses a problem for NLP tools, which are typically trained on monolingual corpora.

Sentiment Analysis Word Embeddings

What Should/Do/Can LSTMs Learn When Parsing Auxiliary Verb Constructions?

1 code implementation CL (ACL) 2020 Miryam de Lhoneux, Sara Stymne, Joakim Nivre

We find that the parser learns different information about AVCs and FMVs if only sequential models (BiLSTMs) are used in the architecture but similar information when a recursive layer is used.

Dependency Parsing

An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing

no code implementations EMNLP 2018 Aaron Smith, Miryam de Lhoneux, Sara Stymne, Joakim Nivre

We provide a comprehensive analysis of the interactions between pre-trained word embeddings, character models and POS tags in a transition-based dependency parser.

Dependency Parsing POS +2

Parser Training with Heterogeneous Treebanks

1 code implementation ACL 2018 Sara Stymne, Miryam de Lhoneux, Aaron Smith, Joakim Nivre

How to make the most of multiple heterogeneous treebanks when training a monolingual dependency parser is an open question.

Arc-Hybrid Non-Projective Dependency Parsing with a Static-Dynamic Oracle

1 code implementation WS 2017 Miryam de Lhoneux, Sara Stymne, Joakim Nivre

In this paper, we extend the arc-hybrid system for transition-based parsing with a swap transition that enables reordering of the words and construction of non-projective trees.

Dependency Parsing

Eye Tracking as a Tool for Machine Translation Error Analysis

no code implementations LREC 2012 Sara Stymne, Henrik Danielsson, Sofia Bremin, Hongzhan Hu, Johanna Karlsson, Anna Prytz Lillkull, Martin Wester

We present a preliminary study where we use eye tracking as a complement to machine translation (MT) error analysis, the task of identifying and classifying MT errors.

Machine Translation Reading Comprehension +1

On the practice of error analysis for machine translation evaluation

no code implementations LREC 2012 Sara Stymne, Lars Ahrenberg

Error analysis is a means to assess machine translation output in qualitative terms, which can be used as a basis for the generation of error profiles for different systems.

Machine Translation Translation

Alignment-based reordering for SMT

no code implementations LREC 2012 Maria Holmqvist, Sara Stymne, Lars Ahrenberg, Magnus Merkel

The reordered text is used to create a second word alignment which can be an improvement of the first alignment, since the word order is more similar.

Machine Translation Part-Of-Speech Tagging +2

Cannot find the paper you are looking for? You can Submit a new open access paper.