Towards Large Language Model driven Reference-less Translation Evaluation for English and Indian Languages

With the primary focus on evaluating the effectiveness of large language models for automatic reference-less translation assessment, this work presents our experiments on mimicking human direct assessment to evaluate the quality of translations in English and Indian languages. We constructed a translation evaluation task where we performed zero-shot learning, in-context example-driven learning, and fine-tuning of large language models to provide a score out of 100, where 100 represents a perfect translation and 1 represents a poor translation. We compared the performance of our trained systems with existing methods such as COMET, BERT-Scorer, and LABSE, and found that the LLM-based evaluator (LLaMA-2-13B) achieves a comparable or higher overall correlation with human judgments for the considered Indian language pairs.

Automatic Data Retrieval for Cross Lingual Summarization

Cross-lingual summarization involves the summarization of text written in one language to a different one. There is a body of research addressing cross-lingual summarization from English to other European languages. In this work, we aim to perform cross-lingual summarization from English to Hindi. We propose pairing up the coverage of newsworthy events in textual and video format can prove to be helpful for data acquisition for cross lingual summarization. We analyze the data and propose methods to match articles to video descriptions that serve as document and summary pairs. We also outline filtering methods over reasonable thresholds to ensure the correctness of the summaries. Further, we make available 28,583 mono and cross-lingual article-summary pairs https://github.com/tingc9/Cross-Sum-News-Aligned. We also build and analyze multiple baselines on the collected data and report error analysis.

Verb Categorisation for Hindi Word Problem Solving

Word problem Solving is a challenging NLP task that deals with solving mathematical problems described in natural language. Recently, there has been renewed interest in developing word problem solvers for Indian languages. As part of this paper, we have built a Hindi arithmetic word problem solver which makes use of verbs. Additionally, we have created verb categorization data for Hindi. Verbs are very important for solving word problems with addition/subtraction operations as they help us identify the set of operations required to solve the word problems. We propose a rule-based solver that uses verb categorisation to identify operations in a word problem and generate answers for it. To perform verb categorisation, we explore several approaches and present a comparative study.

Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey

Generic text summarization approaches often fail to address the specific intent and needs of individual users. Recently, scholarly attention has turned to the development of summarization methods that are more closely tailored and controlled to align with specific objectives and user needs. While a growing corpus of research is devoted towards a more controllable summarization, there is no comprehensive survey available that thoroughly explores the diverse controllable aspects or attributes employed in this context, delves into the associated challenges, and investigates the existing solutions. In this survey, we formalize the Controllable Text Summarization (CTS) task, categorize controllable aspects according to their shared characteristics and objectives, and present a thorough examination of existing methods and datasets within each category. Moreover, based on our findings, we uncover limitations and research gaps, while also delving into potential solutions and future directions for CTS.

Technology Pipeline for Large Scale Cross-Lingual Dubbing of Lecture Videos into Multiple Indian Languages

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%.

Building Odia Shallow Parser

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.

Code-Mixed Sentiment Analysis Using Machine Learning and Neural Network Approaches

Sentiment Analysis for Indian Languages (SAIL)-Code Mixed tools contest aimed at identifying the sentence level sentiment polarity of the code-mixed dataset of Indian languages pairs (Hi-En, Ben-Hi-En). Hi-En dataset is henceforth referred to as HI-EN and Ben-Hi-En dataset as BN-EN respectively. For this, we submitted four models for sentiment analysis of code-mixed HI-EN and BN-EN datasets. The first model was an ensemble voting classifier consisting of three classifiers - linear SVM, logistic regression and random forests while the second one was a linear SVM. Both the models used TF-IDF feature vectors of character n-grams where n ranged from 2 to 6. We used scikit-learn (sklearn) machine learning library for implementing both the approaches. Run1 was obtained from the voting classifier and Run2 used the linear SVM model for producing the results. Out of the four submitted outputs Run2 outperformed Run1 in both the datasets. We finished first in the contest for both HI-EN with an F-score of 0.569 and BN-EN with an F-score of 0.526.

Building a Kannada POS Tagger Using Machine Learning and Neural Network Models

POS Tagging serves as a preliminary task for many NLP applications. Kannada is a relatively poor Indian language with very limited number of quality NLP tools available for use. An accurate and reliable POS Tagger is essential for many NLP tasks like shallow parsing, dependency parsing, sentiment analysis, named entity recognition. We present a statistical POS tagger for Kannada using different machine learning and neural network models. Our Kannada POS tagger outperforms the state-of-the-art Kannada POS tagger by 6%. Our contribution in this paper is three folds - building a generic POS Tagger, comparing the performances of different modeling techniques, exploring the use of character and word embeddings together for Kannada POS Tagging.

Arithmetic Word Problem Solver using Frame Identification

Automatic Word problem solving has always posed a great challenge for the NLP community. Usually a word problem is a narrative comprising of a few sentences and a question is asked about a quantity referred in the sentences. Solving word problem involves reasoning across sentences, identification of operations, their order, relevant quantities and discarding irrelevant quantities. In this paper, we present a novel approach for automatic arithmetic word problem solving. Our approach starts with frame identification. Each frame can either be classified as a state or an action frame. The frame identification is dependent on the verb in a sentence. Every frame is unique and is identified by its slots. The slots are filled using dependency parsed output of a sentence. The slots are entity holder, entity, quantity of the entity, recipient, additional information like place, time. The slots and frames helps to identify the type of question asked and the entity referred. Action frames act on state frame(s) which causes a change in quantities of the state frames. The frames are then used to build a graph where any change in quantities can be propagated to the neighboring nodes. Most of the current solvers can only answer questions related to the quantity, while our system can answer different kinds of questions like `who', `what' other than the quantity related questions `how many'. There are three major contributions of this paper. 1. Frame Annotated Corpus (with a frame annotation tool) 2. Frame Identification Module 3. A new easily understandable Framework for word problem solving