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Found 37 papers, 9 papers with code
Quantifying Synthesis and Fusion and their Impact on Machine Translation
In this work, we propose to reduce the rigidity of such claims, by quantifying morphological typology at the word and segment level.
Does Typological Blinding Impede Cross-Lingual Sharing?
Our hypothesis is that a model trained in a cross-lingual setting will pick up on typological cues from the input data, thus overshadowing the utility of explicitly using such features.
SIGTYP 2020 Shared Task: Prediction of Typological Features
Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world's languages.
Unsupervised Evaluation for Question Answering with Transformers
We observe a consistent pattern in the answer representations, which we show can be used to automatically evaluate whether or not a predicted answer span is correct.
Inducing Language-Agnostic Multilingual Representations
Cross-lingual representations have the potential to make NLP techniques available to the vast majority of languages in the world.
SubjQA: A Dataset for Subjectivity and Review Comprehension
We find that subjectivity is also an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance.
Zero-Shot Cross-Lingual Transfer with Meta Learning
We show that this challenging setup can be approached using meta-learning, where, in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first.
Back to the Future -- Sequential Alignment of Text Representations
In particular, language evolution causes data drift between time-steps in sequential decision-making tasks.
Transductive Auxiliary Task Self-Training for Neural Multi-Task Models
Multi-task learning and self-training are two common ways to improve a machine learning model's performance in settings with limited training data.
Uncovering Probabilistic Implications in Typological Knowledge Bases
The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have post-positions.
A Probabilistic Generative Model of Linguistic Typology
In the principles-and-parameters framework, the structural features of languages depend on parameters that may be toggled on or off, with a single parameter often dictating the status of multiple features.
What do Language Representations Really Represent?
If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations.
Multitask and Multilingual Modelling for Lexical Analysis
In Natural Language Processing (NLP), one traditionally considers a single task (e. g. part-of-speech tagging) for a single language (e. g. English) at a time.
Copenhagen at CoNLL--SIGMORPHON 2018: Multilingual Inflection in Context with Explicit Morphosyntactic Decoding
This paper documents the Team Copenhagen system which placed first in the CoNLL--SIGMORPHON 2018 shared task on universal morphological reinflection, Task 2 with an overall accuracy of 49. 87.
Parameter sharing between dependency parsers for related languages
We find that sharing transition classifier parameters always helps, whereas the usefulness of sharing word and/or character LSTM parameters varies.
Character-level Supervision for Low-resource POS Tagging
Neural part-of-speech (POS) taggers are known to not perform well with little training data.
KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets
We take a multi-task learning approach to the shared Task 1 at SemEval-2018.
Cross-lingual complex word identification with multitask learning
We approach the 2018 Shared Task on Complex Word Identification by leveraging a cross-lingual multitask learning approach.
From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings
A core part of linguistic typology is the classification of languages according to linguistic properties, such as those detailed in the World Atlas of Language Structure (WALS).
Tracking Typological Traits of Uralic Languages in Distributed Language Representations
Although linguistic typology has a long history, computational approaches have only recently gained popularity.
One Model to Rule them all: Multitask and Multilingual Modelling for Lexical Analysis
For instance, if you are a skilled violinist, you will likely have an easier time learning to play cello.
Neural Networks and Spelling Features for Native Language Identification
We present the RUG-SU team{'}s submission at the Native Language Identification Shared Task 2017.
The Power of Character N-grams in Native Language Identification
In this paper, we explore the performance of a linear SVM trained on language independent character features for the NLI Shared Task 2017.
ResSim at SemEval-2017 Task 1: Multilingual Word Representations for Semantic Textual Similarity
Shared Task 1 at SemEval-2017 deals with assessing the semantic similarity between sentences, either in the same or in different languages.
SU-RUG at the CoNLL-SIGMORPHON 2017 shared task: Morphological Inflection with Attentional Sequence-to-Sequence Models
This paper describes the Stockholm University/University of Groningen (SU-RUG) system for the SIGMORPHON 2017 shared task on morphological inflection.
Articulation rate in Swedish child-directed speech increases as a function of the age of the child even when surprisal is controlled for
In earlier work, we have shown that articulation rate in Swedish child-directed speech (CDS) increases as a function of the age of the child, even when utterance length and differences in articulation rate between subjects are controlled for.
The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations
The Parallel Meaning Bank is a corpus of translations annotated with shared, formal meaning representations comprising over 11 million words divided over four languages (English, German, Italian, and Dutch).
Morphological Complexity Influences Verb-Object Order in Swedish Sign Language
Computational linguistic approaches to sign languages could benefit from investigating how complexity influences structure.
Byte-based Language Identification with Deep Convolutional Networks
The system, named ResIdent, is trained only on the data released with the task (closed training).
Semantic Tagging with Deep Residual Networks
We propose a novel semantic tagging task, sem-tagging, tailored for the purpose of multilingual semantic parsing, and present the first tagger using deep residual networks (ResNets).
