ymoslem/acl-abstracts huggingface dataset:
DialogActs based Search and Retrieval for Response Generation in Conversation Systems (2021)

Designing robust conversation systems with great customer experience requires a team of design experts to think of all probable ways a customer can interact with the system and then author responses for each use case individually. The responses are authored from scratch for each new client and application even though similar responses have been created in the past. This happens largely because the responses are encoded using domain specific set of intents and entities. In this paper, we present preliminary work to define a dialog act schema to merge and map responses from different domains and applications using a consistent domain-independent representation. These representations are stored and maintained using an Elasticsearch system to facilitate generation of responses through a search and retrieval process. We experimented generating different surface realizations for a response given a desired information state of the dialog.
An On-device Deep-Learning Approach for Attribute Extraction from Heterogeneous Unstructured Text (2021)

Mobile devices, with their rapidly growing usage, have turned into rich sources of user information, holding critical insights for betterment of user experience and personalization. Creating, receiving and storing important information in the form of unstructured text has become a part and parcel of daily routine of users. From purchase deliveries in Short Message Service (SMS) or Notifications, to event booking details in Calendar applications, mobile devices serve as a portal for understanding user interests, behaviours and activities through information extraction. In this paper, we address the challenge of on-device extraction of user information from unstructured data in natural language from heterogeneous sources like messages, notification, calendar etc. The issue of privacy concern is effectively eliminated by the on-device nature of the proposed solution. Our proposed solution consists of 3 components {--} A Na {\ensuremath{\ddot{}}}{\i}ve-Bayes based classifier for domain identification, a Dual Character andWord based Bidirectional Long Short Term Memory (Bi-LSTM) and Conditional Random Field (CRF) model for attribute extraction and a rule-based Entity Linker. Our solution achieved a 93.29{\%} F1 score on five domains (shopping, travel, event, service and personal). Since on-device deployment has memory and latency constraints, we ensure minimal model size and optimal inference latency. To demonstrate the efficacy of our approach, we have experimented on CoNLL- 2003 dataset and achieved comparable performance to existing benchmark results.
Weakly Supervised Extraction of Tasks from Text (2021)

In this paper, we propose a novel problem of automatic extraction of tasks from text. A task is a well-defined knowledge-based volitional action. We describe various characteristics of tasks as well as compare and contrast them with events. We propose two techniques for task extraction {--} i) using linguistic patterns and ii) using a BERT-based weakly supervised neural model. We evaluate our techniques with other competent baselines on 4 datasets from different domains. Overall, the BERT-based weakly supervised neural model generalizes better across multiple domains as compared to the purely linguistic patterns based approach.
A German Corpus of Reflective Sentences (2021)

Reflection about a learning process is beneficial to students in higher education (Bub-nys, 2019). The importance of machine understanding of reflective texts grows as applications supporting students become more widespread. Nevertheless, due to the sensitive content, there is no public corpus available yet for the classification of text reflectiveness. We provide the first open-access corpus of reflective student essays in German. We collected essays from three different disciplines (Software Development, Ethics of Artificial Intelligence, and Teacher Training). We annotated the corpus at sentence level with binary reflective/non-reflective labels, using an iterative annotation process with linguistic and didactic specialists, mapping the reflective components found in the data to existing schemes and complementing them. We propose and evaluate linguistic features of reflectiveness and analyse their distribution within the resulted sentences according to their labels. Our contribution constitutes the first open-access corpus to help the community towards a unified approach for reflection detection.
Analysis of Manipuri Tones in ManiTo: A Tonal Contrast Database (2021)

Manipuri is a low-resource, tonal language spoken predominantly in Manipur, a northeastern state of India. It has two tones - level and falling tones. For an acceptable Automatic Speech Recognition (ASR) system, integration of tonal information from a robust Tone Recognition model is essential. Research work on ASR has been done on Asian, African and Indo-European tonal languages such as Mandarin, Thai, Vietnamese and Chinese but Manipuri is largely unexplored. This paper focuses on the fundamental analysis of the developed hand-crafted tonal contrast dataset, ManiTo. It is observed that the height and slope of the pitch contour can be used to distinguish the two tones of the Manipuri language.
Building a Linguistic Resource : A Word Frequency List for Sinhala (2021)

A word frequency list is a list of unique words in a language along with their frequency count. It is generally sorted by frequency. Such a list is essential for many NLP tasks, including building language models, POS taggers, spelling checkers, word separation guides, etc., in addition to assisting language learners. Such lists are available for many languages, but a large-scale word list is still not available for Sinhala. We have developed a comprehensive list of words, together with their frequency and part-of-speech (POS), from a large textbase. Unlike many other such lists, our list includes a large number of low-frequency words (many of which are erroneous), which enables the analysis of such words, including the frequencies of errors. In addition to the main list, we have also prepared a list of linguistically verified words. The word frequency list and the verified word list are the largest collections of words lists that are available for the Sinhala language.
Part of Speech Tagging for a Resource Poor Language : Sindhi in Devanagari Script using HMM and CRF (2021)

Part of speech tagging is a pre-processing step of various NLP applications. Mainly it is used in Machine Translation. This research proposes two POS taggers, i.e., an HMM-based and CRF based tagger. To develop this tagger, the corpus of manually annotated 30,000 sentences has been prepared with the help of language experts. In this paper, we have developed POS taggers for Sindhi Language (in Devanagari Script), a resource poor language, using HMM (Hidden Markov Model) and Conditional Random Field (CRF).Evaluation results demonstrated the accuracies of 76.60714{\%} and 88.79{\%} in the HMM, and CRF, respectively.
Stress Rules from Surface Forms: Experiments with Program Synthesis (2021)

Learning linguistic generalizations from only a few examples is a challenging task. Recent work has shown that program synthesis {--} a method to learn rules from data in the form of programs in a domain-specific language {--} can be used to learn phonological rules in highly data-constrained settings. In this paper, we use the problem of phonological stress placement as a case to study how the design of the domain-specific language influences the generalization ability when using the same learning algorithm. We find that encoding the distinction between consonants and vowels results in much better performance, and providing syllable-level information further improves generalization. Program synthesis, thus, provides a way to investigate how access to explicit linguistic information influences what can be learnt from a small number of examples.
Cross-lingual Alignment of Knowledge Graph Triples with Sentences (2021)

The pairing of natural language sentences with knowledge graph triples is essential for many downstream tasks like data-to-text generation, facts extraction from sentences (semantic parsing), knowledge graph completion, etc. Most existing methods solve these downstream tasks using neural-based end-to-end approaches that require a large amount of well-aligned training data, which is difficult and expensive to acquire. Recently various unsupervised techniques have been proposed to alleviate this alignment step by automatically pairing the structured data (knowledge graph triples) with textual data. However, these approaches are not well suited for low resource languages that provide two major challenges: (1) unavailability of pair of triples and native text with the same content distribution and (2) limited Natural language Processing (NLP) resources. In this paper, we address the unsupervised pairing of knowledge graph triples with sentences for low resource languages, selecting Hindi as the low resource language. We propose cross-lingual pairing of English triples with Hindi sentences to mitigate the unavailability of content overlap. We propose two novel approaches: NER-based filtering with Semantic Similarity and Key-phrase Extraction with Relevance Ranking. We use our best method to create a collection of 29224 well-aligned English triples and Hindi sentence pairs. Additionally, we have also curated 350 human-annotated golden test datasets for evaluation. We make the code and dataset publicly available.
Introduction to ProverbNet: An Online Multilingual Database of Proverbs and Comprehensive Metadata (2021)

Proverbs are unique linguistic expressions used by humans in the process of communication. They are frozen expressions and have the capacity to convey deep semantic aspects of a given language. This paper describes ProverbNet, a novel online multilingual database of proverbs and comprehensive metadata equipped with a multipurpose search engine to store, explore, understand, classify and analyze proverbs and their metadata. ProverbNet has immense applications including machine translation, cognitive studies and learning tools. We have 2320 Sanskrit Proverbs and 1136 Marathi proverbs and their metadata in ProverbNet and are adding more proverbs in different languages to the network.
Bypassing Optimization Complexity through Transfer Learning \& Deep Neural Nets for Speech Intelligibility Improvement (2021)

This extended abstract highlights the research ventures and findings in the domain of speech intelligibility improvement. Till this point, an effort has been to simulate the Lombard effect, which is the deliberate human attempt to make a speech more intelligible when speaking in the presence of interfering background noise. To that end, an attempt has been made to shift the formants away from the noisy regions in spectrum both sub-optimally and optimally. The sub-optimal shifting methods were based upon Kalman filtering and EM approach. The optimal shifting involved the use of optimization to maximize an objective intelligibility index after shifting the formants. A transfer learning framework was also set up to bring down the computational complexity.
Design and Development of Spoken Dialogue System in Indic Languages (2021)

Based on the modular architecture of a task-oriented Spoken Dialogue System (SDS), the presented work focussed on constructing all the system components as statistical models with parameters learned directly from the data by resolving various language-specific and language-independent challenges. In order to understand the research questions that underlie the SLU and DST module in the perspective of Indic languages (Hindi), we collect a dialogue corpus: Hindi Dialogue Restaurant Search (HDRS) corpus and compare various state-of-the-art SLU and DST models on it. For the dialogue manager (DM), we investigate the deep-learning reinforcement learning (RL) methods, e.g. actor-critic algorithms with experience replay. Next, for the dialogue generation, we incorporated Recurrent Neural Network Language Generation (RNNLG) framework based models. For speech synthesisers as a last component in the dialogue pipeline, we not only train several TTS systems but also propose a quality assessment framework to evaluate them.
FinRead: A Transfer Learning Based Tool to Assess Readability of Definitions of Financial Terms (2021)

Simplified definitions of complex terms help learners to understand any content better. Comprehending readability is critical for the simplification of these contents. In most cases, the standard formula based readability measures do not hold good for measuring the complexity of definitions of financial terms. Furthermore, some of them works only for corpora of longer length which have at least 30 sentences. In this paper, we present a tool for evaluating readability of definitions of financial terms. It consists of a Light GBM based classification layer over sentence embeddings (Reimers et al., 2019) of FinBERT (Araci, 2019). It is trained on glossaries of several financial textbooks and definitions of various financial terms which are available on the web. The extensive evaluation shows that it outperforms the standard benchmarks by achieving a AU-ROC score of 0.993 on the validation set.
Demo of the Linguistic Field Data Management and Analysis System - LiFE (2021)

In the proposed demo, we will present a new software - Linguistic Field Data Management and Analysis System - LiFE - an open-source, web-based linguistic data management and analysis application that allows for systematic storage, management, sharing and usage of linguistic data collected from the field. The application allows users to store lexical items, sentences, paragraphs, audio-visual content including photographs, video clips, speech recordings, etc, along with rich glossing / annotation; generate interactive and print dictionaries; and also train and use natural language processing tools and models for various purposes using this data. Since its a web-based application, it also allows for seamless collaboration among multiple persons and sharing the data, models, etc with each other. The system uses the Python-based Flask framework and MongoDB (as database) in the backend and HTML, CSS and Javascript at the frontend. The interface allows creation of multiple projects that could be shared with the other users. At the backend, the application stores the data in RDF format so as to allow its release as Linked Data over the web using semantic web technologies - as of now it makes use of the OntoLex-Lemon for storing the lexical data and Ligt for storing the interlinear glossed text and then internally linking it to the other linked lexicons and databases such as DBpedia and WordNet. Furthermore it provides support for training the NLP systems using scikit-learn and HuggingFace Transformers libraries as well as make use of any model trained using these libraries - while the user interface itself provides limited options for tuning the system, an externally-trained model could be easily incorporated within the application; similarly the dataset itself could be easily exported into a standard machine-readable format like JSON or CSV that could be consumed by other programs and pipelines. The system is built as an online platform; however since we are making the source code available, it could be installed by users on their internal / personal servers as well.
Text Based Smart Answering System in Agriculture using RNN (2021)

Agriculture is an important aspect of India`s economy, and the country currently has one of the highest rates of farm producers in the world. Farmers need hand holding with support of technology. A chatbot is a tool or assistant that you may communicate with via instant messages. The goal of this project is to create a Chatbot that uses Natural Language Processing with a Deep Learning model. In this project we have tried implementing Multi-Layer Perceptron model and Recurrent Neural Network models on the dataset. The accuracy given by RNN was 97.83{\%}.
Image2tweet: Datasets in Hindi and English for Generating Tweets from Images (2021)

Image Captioning as a task that has seen major updates over time. In recent methods, visual-linguistic grounding of the image-text pair is leveraged. This includes either generating the textual description of the objects and entities present within the image in constrained manner, or generating detailed description of these entities as a paragraph. But there is still a long way to go towards being able to generate text that is not only semantically richer, but also contains real world knowledge in it. This is the motivation behind exploring image2tweet generation through the lens of existing image-captioning approaches. At the same time, there is little research in image captioning in Indian languages like Hindi. In this paper, we release Hindi and English datasets for the task of tweet generation given an image. The aim is to generate a specialized text like a tweet, that is not a direct result of visual-linguistic grounding that is usually leveraged in similar tasks, but conveys a message that factors-in not only the visual content of the image, but also additional real world contextual information associated with the event described within the image as closely as possible. Further, We provide baseline DL models on our data and invite researchers to build more sophisticated systems for the problem.
ComMA@ICON: Multilingual Gender Biased and Communal Language Identification Task at ICON-2021 (2021)

This paper presents the findings of the ICON-2021 shared task on Multilingual Gender Biased and Communal Language Identification, which aims to identify aggression, gender bias, and communal bias in data presented in four languages: Meitei, Bangla, Hindi and English. The participants were presented the option of approaching the task as three separate classification tasks or a multi-label classification task or a structured classification task. If approached as three separate classification tasks, the task includes three sub-tasks: aggression identification (sub-task A), gender bias identification (sub-task B), and communal bias identification (sub-task C). For this task, the participating teams were provided with a total dataset of approximately 12,000, with 3,000 comments across each of the four languages, sourced from popular social media sites such as YouTube, Twitter, Facebook and Telegram and the the three labels presented as a single tuple. For the test systems, approximately 1,000 comments were provided in each language for every sub-task. We attracted a total of 54 registrations in the task, out of which 11 teams submitted their test runs. The best system obtained an overall instance-F1 of 0.371 in the multilingual test set (it was simply a combined test set of the instances in each individual language). In the individual sub-tasks, the best micro f1 scores are 0.539, 0.767 and 0.834 respectively for each of the sub-task A, B and C. The best overall, averaged micro f1 is 0.713. The results show that while systems have managed to perform reasonably well in individual sub-tasks, especially gender bias and communal bias tasks, it is substantially more difficult to do a 3-class classification of aggression level and even more difficult to build a system that correctly classifies everything right. It is only in slightly over 1/3 of the instances that most of the systems predicted the correct class across the board, despite the fact that there was a significant overlap across the three sub-tasks.
Team\_BUDDI at ComMA@ICON: Exploring Individual and Joint Modelling Approaches for Detecting Aggression, Communal Bias and Gender Bias (2021)

The ComMA@ICON 2021 Shared Task involved identifying the level of aggression and identifying gender bias and communal bias from texts in various languages from the domain of social media. In this paper, we present the description and analyses of systems we implemented towards these tasks. We built systems utilizing Transformer-based models, experimented by individually and jointly modelling these tasks, and investigated the performance of a feature engineering method in conjunction with a joint modelling approach. We demonstrate that the joint modelling approaches outperform the individual modelling approach in most cases.
Hypers at ComMA@ICON: Modelling Aggressive, Gender Bias and Communal Bias Identification (2021)

Due to the exponential increasing reach of social media, it is essential to focus on its negative aspects as it can potentially divide society and incite people into violence. In this paper, we present our system description of work on the shared task ComMA@ICON, where we have to classify how aggressive the sentence is and if the sentence is gender-biased or communal biased. These three could be the primary reasons to cause significant problems in society. Our approach utilizes different pretrained models with Attention and mean pooling methods. We were able to get Rank 1 with 0.253 Instance F1 score on Bengali, Rank 2 with 0.323 Instance F1 score on multilingual set, Rank 4 with 0.129 Instance F1 score on meitei and Rank 5 with 0.336 Instance F1 score on Hindi. The source code and the pretrained models of this work can be found here.
Beware Haters at ComMA@ICON: Sequence and Ensemble Classifiers for Aggression, Gender Bias and Communal Bias Identification in Indian Languages (2021)

Aggressive and hate-filled messages are prevalent on the internet more than ever. These messages are being targeted against a person or an event online and making the internet a more hostile environment. Since this issue is widespread across many users and is not only limited to one language, there is a need for automated models with multilingual capabilities to detect such hostile messages on the online platform. In this paper, the performance of our classifiers is described in the Shared Task on Multilingual Gender Biased and Communal Language Identification at ICON 2021. Our team {\textquotedblleft}Beware Haters{\textquotedblright} took part in Hindi, Bengali, Meitei, and Multilingual tasks. Our team used various models like Random Forest, Logistic Regression, Bidirectional Long Short Term Memory, and an ensemble model. Model interpretation tool LIME was used before integrating the models. The instance F1 score of our best performing models for Hindi, Bengali, Meitei, and Multilingual tasks are 0.289, 0.292, 0.322, and 0.294 respectively.
DELab@IIITSM at ICON-2021 Shared Task: Identification of Aggression and Biasness Using Decision Tree (2021)

This paper presents our system description on participation in ICON-2021 Shared Task sub-task 1 on multilingual gender-biased and communal language identification as team name: DELab@IIITSM. We have participated in two language-specific Meitei, Hindi, and one multi-lingualMeitei, Hindi, and Bangla with English code-mixed languages identification task. Our method includes well design pre-processing phase based on the dataset, the frequency-based feature extraction technique TF-IDF which creates the feature vector for each instance using(Decision Tree). We obtained weights are 0.629, 0.625, and 0.632 as the overall micro F1 score for the Hindi, Meitei, and multilingual datasets.
LUC at ComMA-2021 Shared Task: Multilingual Gender Biased and Communal Language Identification without Using Linguistic Features (2021)

This work aims to evaluate the ability that both probabilistic and state-of-the-art vector space modeling (VSM) methods provide to well known machine learning algorithms to identify social network documents to be classified as aggressive, gender biased or communally charged. To this end, an exploratory stage was performed first in order to find relevant settings to test, i.e. by using training and development samples, we trained multiple algorithms using multiple vector space modeling and probabilistic methods and discarded the less informative configurations. These systems were submitted to the competition of the ComMA@ICON`21 Workshop on Multilingual Gender Biased and Communal Language Identification.