Papers Word Alignment
“Word Alignment” 태그가 달린 논문 564편 · 필터 해제
Learning Language Structures through Grounding
Language is highly structured, with syntactic and semantic structures, to some extent, agreed upon by speakers of the same language. With implicit or explicit awareness of such structures, humans can learn and use langua…
Automatic Speech RecognitionDependency ParsingProgram Synthesisspeech-recognition+3Word Alignment as Preference for Machine Translation
The problem of hallucination and omission, a long-standing problem in machine translation (MT), is more pronounced when a large language model (LLM) is used in MT because an LLM itself is susceptible to these phenomena. …
HallucinationLanguage ModellingLarge Language ModelMachine Translation+2Image-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. Existing methods…
Contrastive LearningLanguage ModelingLanguage ModellingPrompt Learning+3Enhancing 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. This paper shows that c…
RetrievalSentenceSentence EmbeddingSentence-Embedding+4DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment
Recent research demonstrates the effectiveness of using pre-trained language models for legal case retrieval. Most of the existing works focus on improving the representation ability for the contextualized embedding of t…
RetrievalSemantic SimilaritySemantic Textual SimilarityWord AlignmentCross-lingual Contextualized Phrase Retrieval
Phrase-level dense retrieval has shown many appealing characteristics in downstream NLP tasks by leveraging the fine-grained information that phrases offer. In our work, we propose a new task formulation of dense retriev…
Contrastive LearningLanguage ModellingLarge Language ModelMachine Translation+2Multilingual Coreference Resolution in Low-resource South Asian Languages
Coreference resolution involves the task of identifying text spans within a discourse that pertain to the same real-world entity. While this task has been extensively explored in the English language, there has been a no…
coreference-resolutionCoreference ResolutionWord AlignmentSelf-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 'traditi…
Bilingual Lexicon InductionCross-Lingual Word EmbeddingsFew-Shot LearningIn-Context Learning+8Constrained Decoding for Cross-lingual Label Projection
Zero-shot cross-lingual transfer utilizing multilingual LLMs has become a popular learning paradigm for low-resource languages with no labeled training data. However, for NLP tasks that involve fine-grained predictions o…
Cross-Lingual TransferEvent Argument Extractionnamed-entity-recognitionNamed Entity Recognition+4Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models
The evolution of Neural Machine Translation (NMT) has been significantly influenced by six core challenges (Koehn and Knowles, 2017), which have acted as benchmarks for progress in this field. This study revisits these c…
Machine TranslationNMTTranslationWord AlignmentExploring Region-Word Alignment in Built-in Detector for Open-Vocabulary Object Detection
Open-vocabulary object detection aims to detect novel categories that are independent from the base categories used during training. Most modern methods adhere to the paradigm of learning vision-language space from a…
Decoderobject-detectionObject DetectionOpen-vocabulary object detection+3Linguistic-Aware Patch Slimming Framework for Fine-grained Cross-Modal Alignment
Cross-modal alignment aims to build a bridge connecting vision and language. It is an important multi-modal task that efficiently learns the semantic similarities between images and texts. Traditional fine-grained al…
cross-modal alignmentCross-Modal RetrievalImage RetrievalImage-to-Text Retrieval+4Aligning and Prompting Everything All at Once for Universal Visual Perception
Vision foundation models have been explored recently to build general-purpose vision systems. However, predominant paradigms, driven by casting instance-level tasks as an object-word alignment, bring heavy cross-modality…
AllObjectobject-detectionObject Detection+5Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss
Several natural phenomena and complex systems are often represented as networks. Discovering their community structure is a fundamental task for understanding these networks. Many algorithms have been proposed, but recen…
AttributeClusteringCommunity DetectionGraph Attention+4CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
Deriving reliable region-word alignment from image-text pairs is critical to learn object-level vision-language representations for open-vocabulary object detection. Existing methods typically rely on pre-trained or self…
Objectobject-detectionObject DetectionObject Discovery+3On Bilingual Lexicon Induction with Large Language Models
Bilingual Lexicon Induction (BLI) is a core task in multilingual NLP that still, to a large extent, relies on calculating cross-lingual word representations. Inspired by the global paradigm shift in NLP towards Large Lan…
Bilingual Lexicon InductionCross-Lingual Word EmbeddingsFew-Shot LearningIn-Context Learning+7Terminology-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. In our submission to the WMT 2…
Language ModelingLanguage ModellingLarge Language ModelMachine Translation+2Improving Translation Faithfulness of Large Language Models via Augmenting Instructions
Large Language Models (LLMs) present strong general capabilities, and a current compelling challenge is stimulating their specialized capabilities, such as machine translation, through low-cost instruction tuning. The st…
Instruction FollowingMachine TranslationTranslationWord AlignmentGloss Alignment Using Word Embeddings
Capturing and annotating Sign language datasets is a time consuming and costly process. Current datasets are orders of magnitude too small to successfully train unconstrained \acf{slt} models. As a result, research has t…
Word AlignmentWord EmbeddingsDoes 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. Since Romansh is an unseen language, we ar…
SentenceWord AlignmentWord EmbeddingsXLM-R