Abstract

Pre-training a language model and then fine-tuning it for downstream tasks has demonstrated state-of-the-art results for various NLP tasks. Pre-training is usually independent of the downstream task, and previous works have shown that this pre-training alone might not be sufficient to capture the task-specific nuances. We propose a way to tailor a pre-trained BERT model for the downstream task via task-specific masking before the standard supervised fine-tuning. For this, a word list is first collected specific to the task. For example, if the task is sentiment classification, we collect a small sample of words representing both positive and negative sentiments. Next, a word’s importance for the task, called the word’s task score, is measured using the word list. Each word is then assigned a probability of masking based on its task score. We experiment with different masking functions that assign the probability of masking based on the word’s task score. The BERT model is further trained on MLM objective, where masking is done using the above strategy. Following this standard supervised fine-tuning is done for different downstream tasks. Results on these tasks show that the selective masking strategy outperforms random masking, indicating its effectiveness.

Abstract

Document summarization aims to create a precise and coherent summary of a text document. Many deep learning summarization models are developed mainly for English, often requiring a large training corpus and efficient pre-trained language models and tools. However, English summarization models for low-resource Indian languages are often limited by rich morphological variation, syntax, and semantic differences. In this paper, we propose GAE-ISUMM, an unsupervised Indic summarization model that extracts summaries from text documents. In particular, our proposed model, GAE-ISUMM uses Graph Autoencoder (GAE) to learn text representations and a document summary jointly. We also provide a manually-annotated Telugu summarization dataset TELSUM, to experiment with our model GAE-ISUMM. Further, we experiment with the most publicly available Indian language summarization datasets to investigate the effectiveness of GAE-ISUMM on other Indian languages. Our experiments of GAE-ISUMM in seven languages make the following observations: (i) it is competitive or better than state-ofthe-art results on all datasets, (ii) it reports benchmark results on TELSUM, and (iii) the inclusion of positional and cluster information in the proposed model improved the performance of summaries. We open-source our dataset and code 1 . Index Terms—component, formatting, style, styling, insert

Abstract

Cross-lingual dubbing of lecture videos requires the transcription of the original audio, correction and removal of disfluencies, domain term discovery, text-to-text translation into the target language, chunking of text using target language rhythm, text-to-speech synthesis followed by isochronous lipsyncing to the original video. This task becomes challenging when the source and target languages belong to different language families, resulting in differences in generated audio duration. This is further compounded by the original speaker’s rhythm, especially for extempore speech. This paper describes the challenges in regenerating English lecture videos in Indian languages semi-automatically. A prototype is developed for dubbing lectures into 9 Indian languages. A mean-opinion-score (MOS) is obtained for two languages, Hindi and Tamil, on two different courses. The output video is compared with the original video in terms of MOS (1-5) and lip synchronisation with scores of 4.09 and 3.74, respectively. The human effort also reduces by 75%.

Abstract

In computer assisted pronunciation training (CAPT), robust automatic models are critical for pronunciation assessment and mispronunciation detection and diagnosis (MDD). In the modelling, besides the audio data of second language (L2) learners, CAPT requires manually annotated ratings of over- all pronunciation quality, and the MDD uses manually anno- tated phonetic transcriptions. Though the pronunciation qual- ity and the mispronunciation are interdependent, to the best of our knowledge, none of the existing corpora contains both ratings and phonetic transcriptions. This could be due to the cost involved in obtaining phonetic transcriptions. However, a corpus with both kinds of information could benefit the re- searchers to obtain robust models by exploring the interde- pendencies. For addressing this, we develop voisTUTOR 2.0 corpus considering the existing voisTUTOR corpus referred to as voisTUTOR 1.0. We obtain phonetic transcriptions man- ually from a linguist for the entire Indian L2 learners’ En- glish audio data (26529 utterances approximately 14 hours) in voisTUTOR 1.0 for which overall quality ratings and bi- nary scores of factors influencing the pronunciation quality are available. A preliminary analysis of voisTUTOR 2.0 sug- gests that the phonetic errors correlated with the ratings and the binar

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative dis- order of the central nervous system identified by motor and non- motor activities abnormalities. PD affects respiration, laryngeal, articulation, resonance, and prosodic aspects of speech production. Detection of PD from speech is a non-invasive approach useful for automatic screening. Perceptual attributes of speech due to PD are manifested as temporal variations in speech. In this regard, current work investigated the use of LSTM and BiLSTM networks with shifted delta cepstral (SDC) features to detect PD from speech. Further in BiLSTM networks, a multi-head attention mechanism is introduced, assuming that each head captures distinct information to detect PD. SDC features obtained from MFCCs, and SFFCCs are used for developing the PD detection system. The performance of the experiments is validated using the PC-GITA database. The experimental results revealed that BiLSTM networks give a rela- tive improvement of 4-5% over the LSTM networks. The use of a multi-head attention mechanism further improved the detection accuracy of the PD detection system, showing that it can capture various discriminative features.

Abstract

Speech systems are sensitive to accent variations. This is especially challenging in the Indian context, with an abundance of languages but a dearth of linguistic studies characterising pronunciation variations. The growing number of L2 English speakers in India reinforces the need to study accents and L1-L2 interactions. We investigate the accents of Indian English (IE) speakers and report in detail our observations, both specific and common to all regions. In particular, we observe the phonemic variations and phonotactics occurring in the speakers' native languages and apply this to their English pronunciations. We demonstrate the influence of 18 Indian languages on IE by comparing the native language pronunciations with IE pronunciations obtained jointly from existing literature studies and phonetically annotated speech of 80 speakers. Consequently, we are able to validate the intuitions of Indian language influences on IE pronunciations by justifying pronunciation rules from the perspective of Indian language phonology. We obtain a comprehensive description in terms of universal and region-specific characteristics of IE, which facilitates accent conversion and adaptation of existing ASR and TTS systems to different Indian accents.

Abstract

Abstract. We consider Lie algebroids over algebraic spaces (in short we call it as a-spaces) by considering the sheaf of Lie-Rinehart algebras. We discuss about properties of universal enveloping algebroid U (OX, L) of a Lie algebroid L over an a-space (X, OX). This is done by sheafification of the presheaf of universal enveloping algebras for Lie-Rinehart algebras. We review the extent to which structure of the universal enveloping algebroid of Lie algebroids (over special a-spaces) resembles a sheaf of bialgebras. In the sequel we present a version of Poincar´e-Birkhoff-Witt theorem and Cartier-Milnor-Moore theorem for the Lie algebroid.

Abstract

Prior work on constructing challenging tabular inference data centered primarily on human annotation or automatic synthetic generation. Both techniques have their own set of issues. Human annotation, despite its diversity and superior reasoning, struggles from scaling concerns. Synthetic data, on the other hand, despite its scalability, suffers from lack of linguistic and reasoning diversity. In this paper, we address both of these concerns by presenting a recasting approach that semi-automatically generates tabular NLI instances. We transform the table2text dataset ToTTo (Parikh et al., 2020) into a tabular NLI dataset using our proposed framework. We demonstrate the use of our recasted data as an evaluation benchmark as well as augmentation data to improve performance on TabFact (Chen et al., 2020b). Furthermore, we test the effectiveness of models trained on our data on the TabFact benchmark in the zeroshot scenario.

Abstract

Code mixing is the linguistic phenomenon where bilingual speakers tend to switch between two or more languages in conversations. Recent work on code-mixing in computational settings has leveraged social media code mixed texts to train NLP models. For capturing the variety of code mixing in, and across corpus, Language ID (LID) tags based measures (CMI) have been proposed. Syntactical variety/patterns of code-mixing and their relationship vis-a-vis computational model’s performance is under explored. In this work, we investigate a collection of English(en)-Hindi(hi) code-mixed datasets from a syntactic lens to propose, SyMCoM, an indicator of syntactic variety in code-mixed text, with intuitive theoretical bounds. We train SoTA en-hi PoS tagger, accuracy of 93.4%, to reliably compute PoS tags on a corpus, and demonstrate the utility of SyMCoM by applying it on various syntactical categories on a collection of datas

Abstract

Existing research on Tabular Natural Language Inference (TNLI) exclusively examines the task in a monolingual setting where the tabular premise and hypothesis are in the same language. However, due to the uneven distribution of text resources on the web across languages, it is common to have the tabular premise in a high resource language and the hypothesis in a low resource language. As a result, we present the challenging task of bilingual Tabular Natural Language Inference (bTNLI), in which the tabular premise and a hypothesis over it are in two separate languages. We construct EI-InfoTabS: an English-Indic bTNLI dataset by translating the textual hypotheses of the English TNLI dataset InfoTabS into eleven major Indian languages. We thoroughly investigate how pre-trained multilingual models learn and perform on EI-InfoTabS. Our study shows that the performance on bTNLI can be close to its monolingual counterpart, with translate-train, translate-test and unified-train being strongly competitive baselines.