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Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

How to Do Things With Words

In this paper, we discuss the details of the various Machine Translation (MT) systems that we have submitted for the English-Marathi LoResMT task. As a part of this task, we have submitted three different Neural Machine Translation (NMT) systems; a Baseline English-Marathi system, a Baseline Marathi-English system, and an English-Marathi system that is based on the back-translation technique. We explore the performance of these NMT systems between English and Marathi languages, which forms a low resource language pair due to unavailability of sufficient parallel data. We also explore the performance of the back-translation technique when the back-translated data is obtained from NMT systems that are trained on a very less amount of data. From our experiments, we observe that the back-translation technique can help improve the MT quality over the baseline for the English-Marathi language pair.

Evaluating the Performance of Back-translation for Low Resource English-Marathi Language Pair: CFILT-IITBombay @ LoResMT 2021

In this paper, we report our work on the creation of a number of lexical resources that are crucial for an interlingua based MT from English to other languages. These lexical resources are in the form of sub-categorization frames, verb knowledge bases and rule templates for establishing semantic relations and speech act like attributes. We have created these resources over a long period of time from Oxford Advanced Learners Dictionary (OALD) [1], VerbNet [2], Princeton WordNet 2.1 [3], LCS database [4], Penn Tree Bank [5], and XTAG lexicon [6]. On the challenging problem of generating interlingua from domain and structure unrestricted English sentences, we are able to demonstrate that the use of these lexical resources makes a difference in terms of accuracy figures.

/pdf/lexical-resources-for-semantics-extraction-43bs0wdmuw.pdf

Lexical Resources for Semantics Extraction

Word Sense Disambiguation (WSD) is one of the open problems in the area of natural language processing. Various supervised, unsupervised and knowledge based approaches have been proposed for automatically determining the sense of a word in a particular context. It has been observed that such approaches often find it difficult to beat the WordNet First Sense (WFS) baseline which assigns the sense irrespective of context. In this paper, we present our work on creating the WFS baseline for Hindi language by manually ranking the synsets of Hindi WordNet. A ranking tool is developed where human experts can see the frequency of the word senses in the sense-tagged corpora and have been asked to rank the senses of a word by using this information and also his/her intuition. The accuracy of WFS baseline is tested on several standard datasets. F-score is found to be 60%, 65% and 55% on Health, Tourism and News datasets respectively. The created rankings can also be used in other NLP applications viz., Machine Translation, Information Retrieval, Text Summarization, etc.

Synset Ranking of Hindi WordNet

We explore the impact of leveraging the relatedness of languages that belong to the same family in NLP models using multilingual fine-tuning. We hypothesize and validate that multilingual fine-tuning of pre-trained language models can yield better performance on downstream NLP applications, compared to models fine-tuned on individual languages. A first of its kind detailed study is presented to track performance change as languages are added to a base language in a graded and greedy (in the sense of best boost of performance) manner; which reveals that careful selection of subset of related languages can significantly improve performance than utilizing all related languages. The Indo-Aryan (IA) language family is chosen for the study, the exact languages being Bengali, Gujarati, Hindi, Marathi, Oriya, Punjabi and Urdu. The script barrier is crossed by simple rule-based transliteration of the text of all languages to Devanagari. Experiments are performed on mBERT, IndicBERT, MuRIL and two RoBERTa-based LMs, the last two being pre-trained by us. Low resource languages, such as Oriya and Punjabi, are found to be the largest beneficiaries of multilingual fine-tuning. Textual Entailment, Entity Classification, Section Title Prediction, tasks of IndicGLUE and POS tagging form our test bed. Compared to monolingual fine tuning we get relative performance improvement of up to 150% in the downstream tasks. The surprise take-away is that for any language there is a particular combination of other languages which yields the best performance, and any additional language is in fact detrimental.

Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages

Authorship attribution is an important task, as it identifies the author of a written text from a set of suspect authors. Different methodologies of anonymous writing, have been discovered with the rising usage of social media. Authorship attribution helps to find the writer of a suspect text from a set of suspects. Different social media platforms such as Twitter, Facebook, Instagram, etc. are used regularly by the users for sharing their daily life activities. Finding the writer of micro-texts is considered the toughest task, due to the shorter length of the suspect piece of text. We present a fusion based convolutional Neural Network model, which works in two parts i) feature extraction, and ii) classification. Firstly, three different types of features are extracted from the input tweet samples. Three different deep-learning based techniques, namely capsule, LSTM, and GRU are used to extract different sets of features. These learnt features are combined together to represent the latent features for the authorship attribution task. Finally the softmax is used for predicting the class labels. Heat-maps for different models, illustrate the relevant text fragments for the prediction task. This enhances the explain-ability of the developed system. A standard Twitter dataset is used for evaluating the performance of the developed systems. The experimental evaluation shows that proposed fusion based network is able to outperform previous methods. The source codes are available at https://github.com/chanchalIITP/AuthorIdentificationFusion.

Pushpak Bhattacharyya

Papers

Evaluating the Performance of Back-translation for Low Resource English-Marathi Language Pair: CFILT-IITBombay @ LoResMT 2021

Lexical Resources for Semantics Extraction

Synset Ranking of Hindi WordNet

Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages

Authorship Attribution Using Capsule-Based Fusion Approach