Word Alignment
84 papers with code • 7 benchmarks • 4 datasets
Word Alignment is the task of finding the correspondence between source and target words in a pair of sentences that are translations of each other.
Source: Neural Network-based Word Alignment through Score Aggregation
Benchmarks
These leaderboards are used to track progress in Word Alignment
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WMT 2016 News
MUSE
Europarl ConcoDisco Dataset
Bangla Word Analogy
Latest papers with no code
Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation
This paper addresses text-supervised semantic segmentation, aiming to learn a model capable of segmenting arbitrary visual concepts within images by using only image-text pairs without dense annotations.
Enhancing Cross-lingual Sentence Embedding for Low-resource Languages with Word Alignment
The field of cross-lingual sentence embeddings has recently experienced significant advancements, but research concerning low-resource languages has lagged due to the scarcity of parallel corpora.
DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment
Most of the existing works focus on improving the representation ability for the contextualized embedding of the [CLS] token and calculate relevance using textual semantic similarity.
Self-Augmented In-Context Learning for Unsupervised Word Translation
Recent work has shown that, while large language models (LLMs) demonstrate strong word translation or bilingual lexicon induction (BLI) capabilities in few-shot setups, they still cannot match the performance of 'traditional' mapping-based approaches in the unsupervised scenario where no seed translation pairs are available, especially for lower-resource languages.
Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss
The model employs k-means as an objective function and utilizes a multi-head Graph Attention Auto-Encoder for decoding the representations.
Terminology-Aware Translation with Constrained Decoding and Large Language Model Prompting
Terminology correctness is important in the downstream application of machine translation, and a prevalent way to ensure this is to inject terminology constraints into a translation system.
Gloss Alignment Using Word Embeddings
As a result, research has turned to TV broadcast content as a source of large-scale training data, consisting of both the sign language interpreter and the associated audio subtitle.
Does mBERT understand Romansh? Evaluating word embeddings using word alignment
We test similarity-based word alignment models (SimAlign and awesome-align) in combination with word embeddings from mBERT and XLM-R on parallel sentences in German and Romansh.
Contextualized Word Vector-based Methods for Discovering Semantic Differences with No Training nor Word Alignment
In this paper, we propose methods for discovering semantic differences in words appearing in two corpora based on the norms of contextualized word vectors.
Investigating the Role of Attribute Context in Vision-Language Models for Object Recognition and Detection
Methods are mostly evaluated in terms of how well object class names are learned, but captions also contain rich attribute context that should be considered when learning object alignment.
