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Machine Translation

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Machine translation is the task of translating a sentence in a source language to a different target language.

Approaches for machine translation can range from rule-based to statistical to neural-based. More recently, encoder-decoder attention-based architectures like BERT have attained major improvements in machine translation.

One of the most popular datasets used to benchmark machine translation systems is the WMT family of datasets. Some of the most commonly used evaluation metrics for machine translation systems include BLEU, METEOR, NIST, and others.

<span style="color:grey; opacity: 0.6">( Image credit: Google seq2seq )</span>

논문

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Machine Translation

논문

Theoretical Analysis of Byte-Pair Encoding

Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddings

Speak2Sign3D: A Multi-modal Pipeline for English Speech to American Sign Language Animation

GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation

TransLaw: Benchmarking Large Language Models in Multi-Agent Simulation of the Collaborative Translation

Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval

Intrinsic vs. Extrinsic Evaluation of Czech Sentence Embeddings: Semantic Relevance Doesn't Help with MT Evaluation

Has Machine Translation Evaluation Achieved Human Parity? The Human Reference and the Limits of Progress

CycleDistill: Bootstrapping Machine Translation using LLMs with Cyclical Distillation

Semantic similarity estimation for domain specific data using BERT and other techniques

Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Search

Optimizing Multilingual Text-To-Speech with Accents & Emotions

Rethinking Hate Speech Detection on Social Media: Can LLMs Replace Traditional Models?

ConsistencyChecker: Tree-based Evaluation of LLM Generalization Capabilities

A Gamified Evaluation and Recruitment Platform for Low Resource Language Machine Translation Systems

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective

Gender Bias in English-to-Greek Machine Translation

TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration

SSA-COMET: Do LLMs Outperform Learned Metrics in Evaluating MT for Under-Resourced African Languages?

Design of intelligent proofreading system for English translation based on CNN and BERT

Prompting LLMs: Length Control for Isometric Machine Translation

ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT

RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation

Knockout LLM Assessment: Using Large Language Models for Evaluations through Iterative Pairwise Comparisons

EuroLLM-9B: Technical Report

Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model Translations

CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation

VietMix: A Naturally Occurring Vietnamese-English Code-Mixed Corpus with Iterative Augmentation for Machine Translation

Limited-Resource Adapters Are Regularizers, Not Linguists

Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation

Detecting Stealthy Backdoor Samples based on Intra-class Distance for Large Language Models

Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport

Enhancing Large Language Models'Machine Translation via Dynamic Focus Anchoring

Cross-Domain Bilingual Lexicon Induction via Pretrained Language Models

Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement

Translation in the Wild