https://github.com/heng-hw/spacap3d
공식 코드Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds
Papers with Code 아카이브
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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hexuandeng/mono4simt
공식 코드Improving Simultaneous Machine Translation with Monolingual Data
https://github.com/hiaac-nlp/capivara
공식 코드https://github.com/hienvuhuy/transcoref
공식 코드Context-Aware Machine Translation with Source Coreference Explanation
https://github.com/hitz-zentroa/truthfulqa-multi
공식 코드Truth Knows No Language: Evaluating Truthfulness Beyond English
https://github.com/hlt-mt/fbk-fairseq
공식 코드https://github.com/hlt-mt/fbk-neutr-eval
공식 코드Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus
https://github.com/hltchkust/chatgpt-evaluation
공식 코드https://github.com/homan-lab/noise-audit-dataset
공식 코드https://github.com/homan-lab/voiced
공식 코드https://github.com/hongyuanluke/chain-of-dictionary
공식 코드Chain-of-Dictionary Prompting Elicits Translation in Large Language Models
https://github.com/howardchenhd/Syntax-awared-NMT
공식 코드Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder
https://github.com/hplt-project/hplt-textpipes
공식 코드An Expanded Massive Multilingual Dataset for High-Performance Language Technologies
https://github.com/hplt-project/opuscleaner
공식 코드https://github.com/hplt-project/opustrainer
공식 코드https://github.com/hsm316/llm_wikipedia
공식 코드https://github.com/huawei-noah/Pretrained-Language-Model
공식 코드Universal Conditional Masked Language Pre-training for Neural Machine Translation
https://github.com/hubreb/imitkd_ast
공식 코드https://github.com/huggingface/nn_pruning
공식 코드https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md
공식 코드https://github.com/huiguanlab/nrccr
공식 코드Cross-Lingual Cross-Modal Retrieval with Noise-Robust Learning
https://github.com/hunter-ddm/stablemoe
공식 코드https://github.com/hunto/reloss
공식 코드https://github.com/husseinmozannar/SOQAL
공식 코드https://github.com/i-machine-think/compositionality_paradox_mt
공식 코드The paradox of the compositionality of natural language: a neural machine translation case study
https://github.com/i-need-sleep/eval_attack
공식 코드Robustness Tests for Automatic Machine Translation Metrics with Adversarial Attacks
https://github.com/iacercalixto/variational_mmt
공식 코드https://github.com/iai-group/recsys2021-crs-questions
공식 코드Generating Usage-related Questions for Preference Elicitation in Conversational Recommender Systems
https://github.com/iampushpdeep/culturaladapteval
공식 코드Translating Across Cultures: LLMs for Intralingual Cultural Adaptation
https://github.com/iampushpdeep/gender-bias-hi-en-eval
공식 코드https://github.com/ianbstewart/multilingual-same-gender-bias
공식 코드Whose wife is it anyway? Assessing bias against same-gender relationships in machine translation
https://github.com/ibm/indo-aryan-language-family-model
공식 코드https://github.com/ibraheem-moosa/mt-ranker
공식 코드MT-Ranker: Reference-free machine translation evaluation by inter-system ranking
https://github.com/ibraheem-moosa/xlm-indic
공식 코드https://github.com/iclr-complex-order/complex-order
공식 코드https://github.com/icpc2022-gypsum/gypsum_code
공식 코드GypSum: Learning Hybrid Representations for Code Summarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.