Abstract

The last decade has witnessed digitization of many government organization’s data. Text summarization of the political discourse, particularly the parliamentary proceedingsis relatively a lesser explored area of research. In this paper,we investigate the role of semantics especially theory of argu-mentation in debate summarization and use it to design a semiautomatic pipeline for generating these summaries. The proposedapproach considers the topic-relevance, argumentative nature,sentiment and context features. We test our approach on thedataset of debates mined from Lok Sabha, the elected house of representatives in India. Our proposed methodology and pipelineshow significant improvement over the high performing popular systems for ROUGE-1, ROUGE-2 and ROUGE-L metrics.

Abstract

n this paper, we describe our machine translation systems submitted to LoResMT2019 Shared Task. Systems were devel-oped for Bhojpuri, Magahi, Sindhi, Lat-vian⇐⇒(English). This paper outlines preprocessing , configuration of the sub-mitted systems and the results produced using the same

Abstract

This paper introduces a gold standard event-annotated dataset of 810 Hindi news article as well as a set of comprehensive guidelines for detecting and annotating events in Hindi. We present our linguistically motivated guideline development process, with a focus on annotator friendliness, that can be replicated for event mention detection for most Indo-Aryan languages. The paper highlights the challenges of detecting event mentions in Hindi given the unique semantic constraints on the syntactic apparatus used for denoting events. Our work as a whole also establishes a language agnostic pipeline for the development of an event annotated corpora and event detection guidelines.

Abstract

Pregroup calculus has been used for the representation of free word order languages (Sanskrit and Hungarian), using a construction called precyclicity. However, restricted word order alternation has not been handled before. This paper aims at introducing and formally expressing three methods of representing word order alternation in the pregroup representation of any language. This paper describes the word order alternation patterns of Hindi, and creates a basic pregroup representation for the language. In doing so, the shortcoming of correct reductions for ungrammatical sentences due to the current apparatus is highlighted, and the aforementioned methods are invoked for a grammatically accurate representation of restricted word order alternation. The replicability of these methods is explained in the representation of adverbs and prepositional phrases in English.

Abstract

Sentiment Analysis and other semantic tasks are commonly used for social media textual analysis to gauge public opinion and make sense from the noise on social media. The language used on social media not only commonly diverges from the formal language, but is compounded by codemixing between languages, especially in large multilingual societies like India. Traditional methods for learning semantic NLP tasks have long relied on end to end task specific training, requiring expensive data creation process, even more so for deep learning methods. This challenge is even more severe for resource scarce texts like codemixed language pairs, with lack of well learnt representations as model priors, and task specific datasets can be few and small in quantities to efficiently exploit recent deep learning approaches. To address above challenges, we introduce curriculum learning strategies for semantic tasks in code-mixed Hindi-English (Hi-En) texts, and investigate various training strategies for enhancing model performance. Our method outperforms the state of the art methods for Hi-En codemixed sentiment analysis by 3.31% accuracy, and also shows better model robustness in terms of convergence, and variance in test performance.

Abstract

The aim of the current study is to propose acoustic features for detection of nasals in continuous speech. Acoustic features that represent certain characteristics of speech production are extracted. Features representing excitation source characteristcs are extracted using zero frequency filtering method. Features representing vocal tract system characteristics are extracted using zero time windowing method. Feature sets are formed by combining certain subsets of the features mentioned above. These feature sets are evaluated for their representativeness of nasals in continuous speech in three different languages, namely, English, Hindi and Telugu. Results show that nasal detection is reliable and consistent across all the languages mentioned above.

Abstract

For voiced speech, significant excitation of the vocal tract system is due to sequence of impulse-like events in the vocal fold vibration. Significant excitations take place around the instants of glottal closure (GCIs), which are often referred as epochs. In literature, most of the epoch extraction algorithms rely on estimation of the excitation source, by attempting to remove the vocal tract system characteristics. Performance of these algorithms depends on the effectiveness of excitation source estimation methods. Algorithms such as zero frequency filtering (ZFF) and speech event detection using the residual excitation and a mean-based signal (SEDREAMS) detect epochs from the smoothened speech signal by exploiting temporal cues. But these algorithms rely on the value of pitch period. The objective of this paper is to develop an algorithm which does not require any apriori information of pitch period, and can be …

Abstract

The most prevalent multilingual Text-to-Speech (TTS) synthesis systems encounter an unnatural speaker shift at the language boundaries. This is observed when they are employed for code-mixed TTS synthesis. For the very fact that the collection of polyglot speech is non-trivial, many alternative approaches have been in focus. Cross-Lingual Voice Conversion (CLVC) has been one of those to generate speech with desired speaker and language identities. Our aim in this paper is to design a light-weighted CLVC framework between a pair of Mandarin-English speakers. CLVC is challenging when compared to traditional Voice Conversion (VC) because of its nature of accommodating unaligned corpus from the source and target speakers. We thus focus on generating a parallel corpus for CLVC and bridging the gap between speakers and languages. We perform a text-independent voice conversion with a three …

Abstract

Majority of the text modelling techniques yield only point estimates of document embeddings and lack in capturing the uncertainty of the estimates. These uncertainties give a notion of how well the embeddings represent a document. We present Bayesian subspace multinomial model (Bayesian SMM), a generative log-linear model that learns to represent documents in the form of Gaussian distributions, thereby encoding the uncertainty in its covariance. Additionally, in the proposed Bayesian SMM, we address a commonly encountered problem of intractability that appears during variational inference in mixed-logit models. We also present a generative Gaussian linear classifier for topic identification that exploits the uncertainty in document embeddings. Our intrinsic evaluation using perplexity measure shows that the proposed Bayesian SMM fits the data better as compared to variational auto-encoder based document model. Our topic identification experiments on speech (Fisher) and text (20Newsgroups) corpora show that the proposed Bayesian SMM is robust to over-fitting on unseen test data. The topic ID results show that the proposed model is significantly better than variational auto-encoder based methods and achieve similar results when compared to fully supervised discriminative models.

Abstract

In this paper we present a novel weighted NMF (WNMF) algorithm for interpolating missing data. The proposed approach has a computational cost equivalent to that of standard NMF and, additionally, has the flexibility to control the degree of interpolation in the missing data regions. Existing WNMF methods do not offer this capability and, thereby, tend to overestimate the values in the masked regions. By constraining the estimates of the missing-data regions, the proposed approach allows for a better trade-off in the interpolation. We further demonstrate the applicability of WNMF and missing data estimation to the problem of speech enhancement. In this preliminary work, we consider the improvement obtainable by applying the proposed method to ideal binary mask-based gain functions. The instrumental quality metrics (PESQ and SNR) clearly indicate the added benefit of the missing data interpolation, compared …