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1. Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections

   He, Junxian; Zhang, Zhisong; Berg-Kirkpatrick, Taylor; Neubig, Graham (July 2019, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics)

   Cross-lingual transfer is an effective way to build syntactic analysis tools in low-resource languages. However, transfer is difficult when transferring to typologically distant languages, especially when neither annotated target data nor parallel corpora are available. In this paper, we focus on methods for cross-lingual transfer to distant languages and propose to learn a generative model with a structured prior that utilizes labeled source data and unlabeled target data jointly. The parameters of source model and target model are softly shared through a regularized log likelihood objective. An invertible projection is employed to learn a new interlingual latent embedding space that compensates for imperfect crosslingual word embedding input. We evaluate our method on two syntactic tasks: part-ofspeech (POS) tagging and dependency parsing. On the Universal Dependency Treebanks, we use English as the only source corpus and transfer to a wide range of target languages. On the 10 languages in this dataset that are distant from English, our method yields an average of 5.2% absolute improvement on POS tagging and 8.3% absolute improvement on dependency parsing over a direct transfer method using state-of-the-art discriminative models. 

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2. Utterance Selection for Optimizing Intelligibility of TTS Voices Trained on ASR Data

   https://doi.org/DOI: 10.21437/Interspeech.2017-465  

   Erica Cooper, Xinyue Wang (January 2017, Interspeech 2017)

   This paper describes experiments in training HMM-based text-to-speech (TTS) voices on data collected for Automatic Speech Recognition (ASR) training. We compare a number of filtering techniques designed to identify the best utterances from a noisy, multi-speaker corpus for training voices, to exclude speech containing noise and to include speech close in nature to more traditionally-collected TTS corpora. We also evaluate the use of automatic speech recognizers for intelligibility assessment in comparison with crowdsourcing methods. While the goal of this work is to develop natural-sounding and intelligible TTS voices in Low Resource Languages (LRLs) rapidly and easily, without the expense of recording data specifically for this purpose, we focus on English initially to identify the best filtering techniques and evaluation methods. We find that, when a large amount of data is available, selecting from the corpus based on criteria such as standard deviation of f0, fast speaking rate, and hypo-articulation produces the most intelligible voices. 

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3. Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs

   https://doi.org/10.1145/3689735  

   Cassano, Federico; Gouwar, John; Lucchetti, Francesca; Schlesinger, Claire; Freeman, Anders; Anderson, Carolyn Jane; Feldman, Molly Q; Greenberg, Michael; Jangda, Abhinav; Guha, Arjun (October 2024, Proceedings of the ACM on Programming Languages)

   Over the past few years, Large Language Models of Code (Code LLMs) have started to have a significant impact on programming practice. Code LLMs are also emerging as building blocks for research in programming languages and software engineering. However, the quality of code produced by a Code LLM varies significantly by programming language. Code LLMs produce impressive results on high-resource programming languages that are well represented in their training data (e.g., Java, Python, or JavaScript), but struggle with low-resource languages that have limited training data available (e.g., OCaml, Racket, and several others). This paper presents an effective approach for boosting the performance of Code LLMs on low-resource languages using semi-synthetic data. Our approach, called MultiPL-T, generates high-quality datasets for low-resource languages, which can then be used to fine-tune any pretrained Code LLM. MultiPL-T translates training data from high-resource languages into training data for low-resource languages in the following way. 1) We use a Code LLM to synthesize unit tests for commented code from a high-resource source language, filtering out faulty tests and code with low test coverage. 2) We use a Code LLM to translate the code from the high-resource source language to a target low-resource language. This gives us a corpus of candidate training data in the target language, but many of these translations are wrong. 3) We use a lightweight compiler to compile the test cases generated in (1) from the source language to the target language, which allows us to filter our obviously wrong translations. The result is a training corpus in the target low-resource language where all items have been validated with test cases. We apply this approach to generate tens of thousands of new, validated training items for five low-resource languages: Julia, Lua, OCaml, R, and Racket, using Python as the source high-resource language. Furthermore, we use an open Code LLM (StarCoderBase) with open training data (The Stack), which allows us to decontaminate benchmarks, train models without violating licenses, and run experiments that could not otherwise be done. Using datasets generated with MultiPL-T, we present fine-tuned versions of StarCoderBase and Code Llama for Julia, Lua, OCaml, R, and Racket that outperform other fine-tunes of these base models on the natural language to code task. We also present Racket fine-tunes for two very recent models, DeepSeek Coder and StarCoder2, to show that MultiPL-T continues to outperform other fine-tuning approaches for low-resource languages. The MultiPL-T approach is easy to apply to new languages, and is significantly more efficient and effective than alternatives such as training longer. 

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4. Surface Statistics of an Unknown Language Indicate How to Parse It

   https://doi.org/10.1162/tacl_a_00248  

   Wang, Dingquan; Eisner, Jason (December 2018, Transactions of the Association for Computational Linguistics)

   We introduce a novel framework for delexicalized dependency parsing in a new language. We show that useful features of the target language can be extracted automatically from an unparsed corpus, which consists only of gold part-of-speech (POS) sequences. Providing these features to our neural parser enables it to parse sequences like those in the corpus. Strikingly, our system has no supervision in the target language. Rather, it is a multilingual system that is trained end-to-end on a variety of other languages, so it learns a feature extractor that works well. We show experimentally across multiple languages: (1) Features computed from the unparsed corpus improve parsing accuracy. (2) Including thousands of synthetic languages in the training yields further improvement. (3) Despite being computed from unparsed corpora, our learned task-specific features beat previous work’s interpretable typological features that require parsed corpora or expert categorization of the language. Our best method improved attachment scores on held-out test languages by an average of 5.6 percentage points over past work that does not inspect the unparsed data (McDonald et al., 2011), and by 20.7 points over past “grammar induction” work that does not use training languages (Naseem et al., 2010). 

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5. A Deep Learning Architecture for Corpus Creation for Telugu Language

   https://doi.org/10.1007/978-981-15-4029-5_1  

   Dhana L. Rao, Venkatesh R. (July 2020, Advances in intelligent systems and computing)

   Many natural languages are on the decline due to the dominance of English as the language of the World Wide Web (WWW), globalized economy, socioeconomic, and political factors. Computational Linguistics offers unprecedented opportunities for preserving and promoting natural languages. However, availability of corpora is essential for leveraging the Computational Linguistics techniques. Only a handful of languages have corpora of diverse genre while most languages are resource-poor from the perspective of the availability of machine-readable corpora. Telugu is one such language, which is the official language of two southern states in India. In this paper, we provide an overview of techniques for assessing language vitality/endangerment, describe existing resources for developing corpora for the Telugu language, discuss our approach to developing corpora, and present preliminary results. 

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