Papers by Adithya Renduchintala
Tied-LoRA: Enhancing parameter efficiency of LoRA with Weight Tying (2024.naacl-long)
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| Challenge: | a new paradigm for low-rank Adaptation (LoRA) uses weight tying and selective training to improve parameter efficiency. |
| Approach: | They propose a paradigm that uses weight tying and selective training to enhance parameter efficiency of Low-rank Adaptation. |
| Outcome: | The proposed paradigm achieves comparable performance to LoRA with reduced model complexity . the proposed paradigm can be used for a variety of tasks and languages . |
An Exploratory Study on Multilingual Quality Estimation (2020.aacl-main)
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Shuo Sun, Marina Fomicheva, Frédéric Blain, Vishrav Chaudhary, Ahmed El-Kishky, Adithya Renduchintala, Francisco Guzmán, Lucia Specia
| Challenge: | Existing approaches to predict the quality of machine translation use language-specific models, but they lack labelled data for each language pair. |
| Approach: | They propose to use scores from translation models to estimate quality of machine translations by predicting the quality of a translation at test time. |
| Outcome: | The proposed models outperform single-language models in less balanced quality label distributions and low-resource settings. |
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data (2021.acl-long)
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Wei-Jen Ko, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Naman Goyal, Francisco Guzmán, Pascale Fung, Philipp Koehn, Mona Diab
| Challenge: | linguistic overlap between low-resource languages and high-resourced languages is a major obstacle for training high-quality machine translation systems. |
| Approach: | They exploit linguistic overlap to facilitate translation to and from low-resource languages . they use monolingual data and parallel data in related high-resourced languages based on their method . |
| Outcome: | The proposed method significantly improves translation into low-resource language compared to baselines on 7 languages from three different language families. |
Gender bias amplification during Speed-Quality optimization in Neural Machine Translation (2021.acl-short)
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| Challenge: | et al., 2002) show that gendered noun translation performance degrades faster than BLEU. |
| Approach: | They propose to use greedy search, quantization, AANs and shallow decoders to speed up decoding . they find minimal degradation of BLEU, but gendered noun translation degrades faster . |
| Outcome: | The proposed model degrades gendered noun translation performance faster than other models. |
Spelling-Aware Construction of Macaronic Texts for Teaching Foreign-Language Vocabulary (D19-1)
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| Challenge: | a machine foreign-language teacher replaces word tokens with glosses in a foreign language to ease the human reader into understanding the L2 vocabulary. |
| Approach: | They propose a machine foreign-language teacher that modifies text by replacing word tokens with glosses in a foreign language to ease the human reader into understanding the L2 . |
| Outcome: | The proposed model can learn representations for novel words and is a proxy for word guessing and learning ability of real human students. |
Multilingual Neural Machine Translation with Deep Encoder and Multiple Shallow Decoders (2021.eacl-main)
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| Challenge: | Recent work in multilingual translation has improved translation quality surpassing bilingual baselines using deep transformer models with increased capacity. |
| Approach: | They propose a deep encoder with multiple shallow decoders to reduce inference latency while maintaining translation quality. |
| Outcome: | The proposed model achieves 1.8x speedup on average compared to a standard transformer model with no drop in translation quality. |
Investigating Failures of Automatic Translation
in the Case of Unambiguous Gender (2022.acl-long)
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| Challenge: | Existing models are unable to make basic deductions regarding how to correctly inflect nouns with grammatical gender. |
| Approach: | They propose to evaluate NMT models' ability to translate gender morphology correctly in unambiguous contexts across syntactically diverse sentences. |
| Outcome: | The proposed model was unable to translate gender morphology correctly in unambiguous contexts across syntactically diverse sentences. |
XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment (2021.emnlp-main)
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| Challenge: | Existing approaches to generate named entity lexica for lower-resource languages are under performing. |
| Approach: | They propose a technique to automatically mine cross-lingual named-entity lexica from mined web data. |
| Outcome: | The proposed technique outperforms baselines at extracting cross-lingual entity pairs and mines 164 million entity pairs from 120 different languages aligned with English. |
Quality Estimation without Human-labeled Data (2021.eacl-main)
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Yi-Lin Tuan, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Francisco Guzmán, Lucia Specia
| Challenge: | Quality estimation aims to measure the quality of translated content without access to a reference translation. |
| Approach: | They propose a method that uses synthetic training data to train supervised quality estimation models. |
| Outcome: | The proposed model outperforms models trained on human-annotated data for sentence and word-level prediction. |