Papers Word Alignment

Neural semi-Markov CRF for Monolingual Word Alignment

Monolingual word alignment is important for studying fine-grained editing operations (i.e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neut…

Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources

While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each langua…

Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention

We describe a span-level supervised attention loss that improves compositional generalization in semantic parsers. Our approach builds on existing losses that encourage attention maps in neural sequence-to-sequence model…

CombAlign: a Tool for Obtaining High-Quality Word Alignments

Being able to generate accurate word alignments is useful for a variety of tasks. While statistical word aligners can work well, especially when parallel training data are plentiful, multilingual embedding models have re…

Word Alignment Dissimilarity Indicator: Alignment Links as Conceptualizations of a Focused Bilingual Lexicon

Embedding-Enhanced Giza++: Improving Alignment in Low- and High- Resource Scenarios Using Embedding Space Geometry

A popular natural language processing task decades ago, word alignment has been dominated until recently by GIZA++, a statistical method based on the 30-year-old IBM models. New methods that outperform GIZA++ primarily r…

XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment

Cross-lingual named-entity lexica are an important resource to multilingual NLP tasks such as machine translation and cross-lingual wikification. While knowledge bases contain a large number of entities in high-resource …

Zero-Shot Cross-lingual Semantic Parsing

Recent work in cross-lingual semantic parsing has successfully applied machine translation to localize parsers to new languages. However, these advances assume access to high-quality machine translation systems and word …

Sparse Attention with Linear Units

Recently, it has been argued that encoder-decoder models can be made more interpretable by replacing the softmax function in the attention with its sparse variants. In this work, we introduce a novel, simple method for a…

Leveraging Neural Machine Translation for Word Alignment

The most common tools for word-alignment rely on a large amount of parallel sentences, which are then usually processed according to one of the IBM model algorithms. The training data is, however, the same as for machine…

MirrorAlign: A Super Lightweight Unsupervised Word Alignment Model via Cross-Lingual Contrastive Learning

Word alignment is essential for the downstream cross-lingual language understanding and generation tasks. Recently, the performance of the neural word alignment models has exceeded that of statistical models. However, th…

Bootstrapping Multilingual AMR with Contextual Word Alignments

We develop high performance multilingualAbstract Meaning Representation (AMR) sys-tems by projecting English AMR annotationsto other languages with weak supervision. Weachieve this goal by bootstrapping transformer-based…

Word Alignment by Fine-tuning Embeddings on Parallel Corpora

Word alignment over parallel corpora has a wide variety of applications, including learning translation lexicons, cross-lingual transfer of language processing tools, and automatic evaluation or analysis of translation o…

Unsupervised Word Alignment via Cross-Lingual Contrastive Learning

Word alignment is essential for the down-streaming cross-lingual language understanding and generation tasks. Recently, the performance of the neural word alignment models has exceeded that of statistical models. However…

Efficient Neural Machine Translation with Prior Word Alignment

Prior word alignment has been shown indeed helpful for a better translation if such prior is good enough and can be acquired in a convenient way at the same time. Traditionally, word alignment can be learned through stat…

Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment

Bilingual lexicons map words in one language to their translations in another, and are typically induced by learning linear projections to align monolingual word embedding spaces. In this paper, we show it is possible to…

XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders

Multilingual machine translation enables a single model to translate between different languages. Most existing multilingual machine translation systems adopt a randomly initialized Transformer backbone. In this work, in…

Subword Sampling for Low Resource Word Alignment

Annotation projection is an important area in NLP that can greatly contribute to creating language resources for low-resource languages. Word alignment plays a key role in this setting. However, most of the existing word…

Mask-Align: Self-Supervised Neural Word Alignment

Word alignment, which aims to align translationally equivalent words between source and target sentences, plays an important role in many natural language processing tasks. Current unsupervised neural alignment methods f…

改善詞彙對齊以擷取片語翻譯之方法 (Improving Word Alignment for Extraction Phrasal Translation)