Abstract

How do language learners avoid the production of verb argument structure overgeneralization errors (*The clown laughed the man c.f. The clown made the man laugh), while retaining the ability to apply such generalizations productively when appropriate? This question has long been seen as one that is both particularly central to acquisition research and particularly challenging. Focussing on causative overgeneralization errors of this type, a previous study reported a computational model that learns, on the basis of corpus data and human-derived verb-semantic-feature ratings, to pred

Abstract

We present the Hindi-Telugu Parallel Corpus of different technical domains such as Natural Science, Computer Science, Law and Healthcare along with the General domain. The qualitative corpus consists of 700K parallel sentences of which 535K sentences were created using multiple methods such as extract, align and review of Hindi-Telugu corpora, end-to-end human translation, iterative back-translation driven post-editing and around 165K parallel sentences were collected from available sources in the public domain. We present the comparative assessment of created parallel corpora for representativeness and diversity. The corpus has been pre-processed for machine translation, and we trained a neural machine translation system using it and report state-of-the-art baseline results on the developed development set over multiple domains and on available benchmarks. With this, we define a new task on Domain Machine Translation for low resource language pairs such as Hindi and Telugu. The developed corpus (535K) is freely available for non-commercial research and to the best of our knowledge, this is the well curated, largest, publicly available domain parallel corpus for Hindi-Telugu.

Abstract

Word Problem Solving remains a challenging and interesting task in NLP. A lot of research has been carried out to solve different genres of word problems with various complexity levels in recent years. However, most of the publicly available datasets and work has been carried out for English. Recently there has been a surge in this area of word problem solving in Chinese with the creation of large benchmark datastes. Apart from these two languages, labeled benchmark datasets for low resource languages are very scarce. This is the first attempt to address this issue for any Indian Language, especially Hindi. In this paper, we present HAWP (Hindi Arithmetic Word Problems), a dataset consisting of 2336 arithmetic word problems in Hindi. We also developed baseline systems for solving these word problems. We also propose a new evaluation technique for word problem solvers taking equation equivalence into account.

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

Code-mixed machine translation has become an important task in multilingual communities and extending the task of machine translation to code mixed data has become a common task for these languages. In the shared tasks of WMT 2022, we try to tackle the same for both English + Hindi to Hinglish and Hinglish to English. The first task dealt with both Roman and Devanagari script as we had monolingual data in both English and Hindi whereas the second task only had data in Roman script. To our knowledge, we achieved one of the top ROUGE-L and WER scores for the first task of Monolingual to Code-Mixed machine translation. In this paper, we discuss the use of mBART with some special pre-processing and post-processing (transliteration from Devanagari to Roman) for the first task in detail and the experiments that we performed for the second task of translating code-mixed Hinglish to monolingual English.

Abstract

Shallow parsing is an essential task for many NLP applications like machine translation, summarization, sentiment analysis, aspect identification and many more. Quality annotated corpora is critical for building accurate shallow parsers. Many Indian languages are resource poor with respect to the availability of corpora in general. So, this paper is an attempt towards creating quality corpora for shallow parsers. The contribution of this paper is two folds: creation pos and chunk annotated corpora for Odia and development of baseline systems for pos tagging and chunking in Odia.

Abstract

In this paper, we present a novel approach for domain adaptation in Neural Machine Translation which aims to improve the translation quality over a new domain. Adapting new domains is a highly challeng- ing task for Neural Machine Translation on limited data, it becomes even more diffi- cult for technical domains such as Chem- istry and Artificial Intelligence due to spe- cific terminology, etc. We propose Domain Specific Back Translation method which uses available monolingual data and gen- erates synthetic data in a different way. This approach uses Out Of Domain words. The approach is very generic and can be applied to any language pair for any do- main. We conduct our experiments on Chemistry and Artificial Intelligence do- mains for Hindi and Telugu in both direc- tions. It has been observed that the usage of synthetic data created by the proposed algorithm improves the BLEU scores sig- nificantly

Abstract

This paper describes our team oneNLP’s (LTRC, IIIT-Hyderabad) participation for the SEPP-NLG 2021 tasks1, Sentence End and Punctuation Prediction in NLG Text-2021. We applied sequence to tag prediction over contextual embedding as fine-tuning for both of these tasks. We also explored the use of multilingual Bert and multitask learning for these tasks on English, German, French and Italian.

Abstract

In this paper, we (team - oneNLP-IIITH) describe our Neural Machine Translation approaches for English-Marathi (both direction) for LoResMT-20211 . We experimented with transformer based Neural Machine Translation and explored the use of different linguistic features like POS and Morph on subword unit for both English-Marathi and Marathi-English. In addition, we have also explored forward and backward translation using web-crawled monolingual data. We obtained 22.2 (overall 2 nd) and 31.3 (overall 1 st) BLEU scores for English-Marathi and Marathi-English on respectively

Abstract

Discourse parsing, which involves understand- ing the structure, information flow, and mod- eling the coherence of a given text, is an im- portant task in natural language processing. It forms the basis of several natural language pro- cessing tasks such as question-answering, text summarization, and sentiment analysis. Dis- course unit segmentation is one of the funda- mental tasks in discourse parsing and refers to identifying the elementary units of text that combine to form a coherent text. In this pa- per, we present a transformer based approach towards the automated identification of dis- course unit segments and connectives. Early approaches towards segmentation relied on rule-based systems using POS tags and other syntactic information to identify discourse seg- ments. Recently, transformer based neural sys- tems have shown promising results in this do- main. Our system, SegFormers, employs this transformer based approach to perform multi- lingual discourse segmentation and connective identification across 16 datasets encompassing 11 languages and 3 different annotation frame- works. We evaluate the system based on F1 scores for both tasks, with the best system re- porting the highest F1 score of 97.02% for the treebanked English RST-DT dataset