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Found 106 papers, 15 papers with code
Cause and Effect in Governmental Reports: Two Data Sets for Causality Detection in Swedish
Causality detection is the task of extracting information about causal relations from text.
To the Most Gracious Highness, from Your Humble Servant: Analysing Swedish 18th Century Petitions Using Text Classification
We also test how text pre-processing and different feature representations affect the result, and we examine feature importance for our main class of interest - petitions.
Universal Dependencies for Albanian
In this paper, we introduce the first Universal Dependencies (UD) treebank for standard Albanian, consisting of 60 sentences collected from the Albanian Wikipedia, annotated with lemmas, universal part-of-speech tags, morphological features and syntactic dependencies.
Fine-Grained Controllable Text Generation Using Non-Residual Prompting
We propose a resource-efficient method for converting a pre-trained CLM into this architecture, and demonstrate its potential on various experiments, including the novel task of contextualized word inclusion.
Overview of MWE history, challenges, and horizons: standing at the 20th anniversary of the MWE workshop series via MWE-UD2024
Starting in 2003 when the first MWE workshop was held with ACL in Sapporo, Japan, this year, the joint workshop of MWE-UD co-located with the LREC-COLING 2024 conference marked the 20th anniversary of MWE workshop events over the past nearly two decades.
The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation
This paper introduces the counter-intuitive generalization results of overfitting pre-trained large language models (LLMs) on very small datasets.
UCxn: Typologically Informed Annotation of Constructions Atop Universal Dependencies
The Universal Dependencies (UD) project has created an invaluable collection of treebanks with contributions in over 140 languages.
Continual Learning Under Language Shift
The recent increase in data and model scale for language model pre-training has led to huge training costs.
Schrödinger's Tree -- On Syntax and Neural Language Models
In the last half-decade, the field of natural language processing (NLP) has undergone two major transitions: the switch to neural networks as the primary modeling paradigm and the homogenization of the training regime (pre-train, then fine-tune).
Revisiting Negation in Neural Machine Translation
In this paper, we evaluate the translation of negation both automatically and manually, in English--German (EN--DE) and English--Chinese (EN--ZH).
Syntactic Nuclei in Dependency Parsing -- A Multilingual Exploration
Standard models for syntactic dependency parsing take words to be the elementary units that enter into dependency relations.
Attention Can Reflect Syntactic Structure (If You Let It)
Since the popularization of the Transformer as a general-purpose feature encoder for NLP, many studies have attempted to decode linguistic structure from its novel multi-head attention mechanism.
Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English
The attention distribution pattern shows that separators attract a lot of attention and we explore a sparse word-level attention to enforce character hidden states to capture the full word-level information.
Greedy Transition-Based Dependency Parsing with Discrete and Continuous Supertag Features
We study the effect of rich supertag features in greedy transition-based dependency parsing.
Principal Word Vectors
The effect of these generalizations on the word vectors is intrinsically studied with regard to the spread and the discriminability of the word vectors.
K\opsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
We present K{\o}psala, the Copenhagen-Uppsala system for the Enhanced Universal Dependencies Shared Task at IWPT 2020.
Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
We present K{\o}psala, the Copenhagen-Uppsala system for the Enhanced Universal Dependencies Shared Task at IWPT 2020.
A Tale of Three Parsers: Towards Diagnostic Evaluation for Meaning Representation Parsing
We discuss methodological choices in contrastive and diagnostic evaluation in meaning representation parsing, i. e. mapping from natural language utterances to graph-based encodings of its semantic structure.
Do Neural Language Models Show Preferences for Syntactic Formalisms?
Recent work on the interpretability of deep neural language models has concluded that many properties of natural language syntax are encoded in their representational spaces.
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection
Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework.
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited
Transition-based and graph-based dependency parsers have previously been shown to have complementary strengths and weaknesses: transition-based parsers exploit rich structural features but suffer from error propagation, while graph-based parsers benefit from global optimization but have restricted feature scope.
Encoders Help You Disambiguate Word Senses in Neural Machine Translation
We find that encoder hidden states outperform word embeddings significantly which indicates that encoders adequately encode relevant information for disambiguation into hidden states.
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited
Transition-based and graph-based dependency parsers have previously been shown to have complementary strengths and weaknesses: transition-based parsers exploit rich structural features but suffer from error propagation, while graph-based parsers benefit from global optimization but have restricted feature scope.
What Should/Do/Can LSTMs Learn When Parsing Auxiliary Verb Constructions?
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.
Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models
In this paper, we try to understand neural machine translation (NMT) via simplifying NMT architectures and training encoder-free NMT models.
Recursive Subtree Composition in LSTM-Based Dependency Parsing
When ablating the forward LSTM, performance drops less dramatically and composition recovers a substantial part of the gap, indicating that a forward LSTM and composition capture similar information.
Expletives in Universal Dependency Treebanks
Although treebanks annotated according to the guidelines of Universal Dependencies (UD) now exist for many languages, the goal of annotating the same phenomena in a cross-linguistically consistent fashion is not always met.
Enhancing Universal Dependency Treebanks: A Case Study
We evaluate two cross-lingual techniques for adding enhanced dependencies to existing treebanks in Universal Dependencies.
An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation
Recent work has shown that the encoder-decoder attention mechanisms in neural machine translation (NMT) are different from the word alignment in statistical machine translation.
CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Every year, the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
82 Treebanks, 34 Models: Universal Dependency Parsing with Multi-Treebank Models
We present the Uppsala system for the CoNLL 2018 Shared Task on universal dependency parsing.
An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing
We provide a comprehensive analysis of the interactions between pre-trained word embeddings, character models and POS tags in a transition-based dependency parser.
Universal Word Segmentation: Implementation and Interpretation
Word segmentation is a low-level NLP task that is non-trivial for a considerable number of languages.
An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization
In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages: English, German, Hungarian, Icelandic, and Swedish.
Parser Training with Heterogeneous Treebanks
How to make the most of multiple heterogeneous treebanks when training a monolingual dependency parser is an open question.
Sentences with Gapping: Parsing and Reconstructing Elided Predicates
Sentences with gapping, such as Paul likes coffee and Mary tea, lack an overt predicate to indicate the relation between two or more arguments.
Recall is the Proper Evaluation Metric for Word Segmentation
We extensively analyse the correlations and drawbacks of conventionally employed evaluation metrics for word segmentation.
Arc-Hybrid Non-Projective Dependency Parsing with a Static-Dynamic Oracle
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.
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
From Raw Text to Universal Dependencies - Look, No Tags!
We present the Uppsala submission to the CoNLL 2017 shared task on parsing from raw text to universal dependencies.
Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF
We present a character-based model for joint segmentation and POS tagging for Chinese.
Universal Dependencies
Universal Dependencies (UD) is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages.
Universal Dependencies for Turkish
The Universal Dependencies (UD) project was conceived after the substantial recent interest in unifying annotation schemes across languages.
Universal Dependencies: A Cross-Linguistic Perspective on Grammar and Lexicon
Universal Dependencies is an initiative to develop cross-linguistically consistent grammatical annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning and parsing research from a language typology perspective.
Universal Dependencies v1: A Multilingual Treebank Collection
Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments.
The Universal Dependencies Treebank of Spoken Slovenian
This paper presents the construction of an open-source dependency treebank of spoken Slovenian, the first syntactically annotated collection of spontaneous speech in Slovenian.
Universal Dependencies for Persian
The Persian Universal Dependency Treebank (Persian UD) is a recent effort of treebanking Persian with Universal Dependencies (UD), an ongoing project that designs unified and cross-linguistically valid grammatical representations including part-of-speech tags, morphological features, and dependency relations.
Static and Dynamic Feature Selection in Morphosyntactic Analyzers
Experiments on five languages show that feature selection can result in more compact models as well as higher accuracy under all conditions, but also that a dynamic ordering works better than a static ordering and that joint systems benefit more than standalone taggers.
Universal Stanford dependencies: A cross-linguistic typology
Revisiting the now de facto standard Stanford dependency representation, we propose an improved taxonomy to capture grammatical relations across languages, including morphologically rich ones.
A Persian Treebank with Stanford Typed Dependencies
We present the Uppsala Persian Dependency Treebank (UPDT) with a syntactic annotation scheme based on Stanford Typed Dependencies.
Token and Type Constraints for Cross-Lingual Part-of-Speech Tagging
We consider the construction of part-of-speech taggers for resource-poor languages.
Training Deterministic Parsers with Non-Deterministic Oracles
This problem is aggravated by the fact that they are normally trained using oracles that are deterministic and incomplete in the sense that they assume a unique canonical path through the transition system and are only valid as long as the parser does not stray from this path.
Joint Morphological and Syntactic Analysis for Richly Inflected Languages
Joint morphological and syntactic analysis has been proposed as a way of improving parsing accuracy for richly inflected languages.
A Basic Language Resource Kit for Persian
As for resources, we describe the Uppsala PErsian Corpus (UPEC) which is a modified version of the Bijankhan corpus with additional sentence segmentation and consistent tokenization modified for more appropriate syntactic annotation.
MaltOptimizer: A System for MaltParser Optimization
Freely available statistical parsers often require careful optimization to produce state-of-the-art results, which can be a non-trivial task especially for application developers who are not interested in parsing research for its own sake.
