Search Results for author:
Found 16 papers, 6 papers with code
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings
We find that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners, even with abundant parallel data; e. g., contextualized embeddings achieve a word alignment F1 for English-German that is 5 percentage points higher than eflomal, a high-quality statistical aligner, trained on 100k parallel sentences.
Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages
The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i. e., making them better for about 100 languages.
Graph Algorithms for Multiparallel Word Alignment
With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently.
CaMEL: Case Marker Extraction without Labels
We introduce CaMEL (Case Marker Extraction without Labels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages.
Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging
An established method for training a POS tagger in such a scenario is to create a labeled training set by transferring from high-resource languages.
Aligning Very Small Parallel Corpora Using Cross-Lingual Word Embeddings and a Monogamy Objective
Count-based word alignment methods, such as the IBM models or fast-align, struggle on very small parallel corpora.
Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries
To this end, first we build a query-generated training data using pseudo-relevant documents to the query and all translation candidates.
Improving Word Alignment of Rare Words with Word Embeddings
We address the problem of inducing word alignment for language pairs by developing an unsupervised model with the capability of getting applied to other generative alignment models.
Subword Sampling for Low Resource Word Alignment
This method's hypothesis is that the aggregation of different granularities of text for certain language pairs can help word-level alignment.
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus
With more than 7000 languages worldwide, multilingual natural language processing (NLP) is essential both from an academic and commercial perspective.
Graph Neural Networks for Multiparallel Word Alignment
First, we create a multiparallel word alignment graph, joining all bilingual word alignment pairs in one graph.
Don't Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings
The two approaches we find most effective are: 1) using identical words as seed lexicons (which unsupervised approaches incorrectly assume are not available for orthographically distinct language pairs) and 2) combining such lexicons with pairs extracted by matching romanized versions of words with an edit distance threshold.
Don’t Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings
The two approaches we find most effective are: 1) using identical words as seed lexicons (which unsupervised approaches incorrectly assume are not available for orthographically distinct language pairs) and 2) combining such lexicons with pairs extracted by matching romanized versions of words with an edit distance threshold.
LICD: A Language-Independent Approach for Aspect Category Detection
For a given sentence, our proposed method performs ACD based on two hypotheses: First, a category should be assigned to a sentence if there is a high semantic similarity between the sentence and a set of representative words of that category.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+6
