Semantic Structural Decomposition for Neural Machine Translation

2020-12-01 · Joint Conference on Lexical and Computational Semantics 2020 · Elior Sulem, Omri Abend, Ari Rappoport

Building on recent advances in semantic parsing and text simplification, we investigate the use of semantic splitting of the source sentence as preprocessing for machine translation. We experiment with a Transformer model and evaluate using large-scale crowd-sourcing experiments. Results show a significant increase in fluency on long sentences on an English-to- French setting with a training corpus of 5M sentence pairs, while retaining comparable adequacy. We also perform a manual analysis which explores the tradeoff between adequacy and fluency in the case where all sentence lengths are considered.

📄 PDF Abstract BibTeX

Code (1)

eliorsulem/semantic-structural-decomposition-for-nmt 공식 구현

Tasks

Machine Translation Semantic ParsingSentenceText Simplification Translation

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…

Position-Wise Feed-Forward Layer 설명 없음

Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…

BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…

Attention 설명 없음

Adam 설명 없음

Phrase database Approach to structural and semantic disambiguation in English-Korean Machine Translation

2015-03-19 · Myong-Chol Pak

In machine translation it is common phenomenon that machine-readable dictionaries and standard parsing rules are not enough to ensure accuracy in parsing and translating English phrases into Korean language, which is rev…

Machine Translation Translation

Evaluating Structural Generalization in Neural Machine Translation

2024-06-19 · Ryoma Kumon, Daiki Matsuoka, Hitomi Yanaka

Compositional generalization refers to the ability to generalize to novel combinations of previously observed words and syntactic structures. Since it is regarded as a desired property of neural models, recent work has a…

Machine Translation Semantic Parsing Sentence Translation

Structural Transfer Learning in NL-to-Bash Semantic Parsers

2023-07-31 · Kyle Duffy, Satwik Bhattamishra, Phil Blunsom

Large-scale pre-training has made progress in many fields of natural language processing, though little is understood about the design of pre-training datasets. We propose a methodology for obtaining a quantitative under…

Machine Translation Semantic Parsing Transfer Learning Translation

Simple and Effective Text Simplification Using Semantic and Neural Methods

2018-10-11 · ACL 2018 7 · Elior Sulem, Omri Abend, Ari Rappoport

Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simpli…

Machine Translation Semantic Parsing Sentence Text Simplification+1

Language-Grounded Multi-Domain Image Translation via Semantic Difference Guidance

2026-01-12 · Jongwon Ryu, Joonhyung Park, Jaeho Han, Yeong-Seok Kim 외 arxiv

Multi-domain image-to-image translation re quires grounding semantic differences ex pressed in natural language prompts into corresponding visual transformations, while preserving unrelated structural and seman tic conte…

Image-to-Image Translation