pwc-archive/papers-with-abstracts huggingface dataset:
Transferring Modern Named Entity Recognition to the Historical Domain: How to Take the Step?
Authors: Baptiste Blouin, Benoit Favre, Jeremy Auguste, Christian Henriot
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Optical Character Recognition (OCR)

Named entity recognition is of high interest to digital humanities, in particular when mining historical documents. Although the task is mature in the field of NLP, results of contemporary models are not satisfactory on challenging documents corresponding to out-of-domain genres, noisy OCR output, or old-variants of the target language. In this paper we study how model transfer methods, in the context of the aforementioned challenges, can improve historical named entity recognition according to how much effort is allocated to describing the target data, manually annotating small amounts of texts, or matching pre-training resources. In particular, we explore the situation where the class labels, as well as the quality of the documents to be processed, are different in the source and target domains. We perform extensive experiments with the transformer architecture on the LitBank and HIPE historical datasets, with different annotation schemes and character-level noise. They show that annotating 250 sentences can recover 93% of the full-data performance when models are pre-trained, that the choice of self-supervised and target-task pre-training data is crucial in the zero-shot setting, and that OCR errors can be handled by simulating noise on pre-training data and resorting to recent character-aware transformers.
Did You Enjoy the Last Supper? An Experimental Study on Cross-Domain NER Models for the Art Domain
Authors: Alejandro Sierra-Múnera, Ralf Krestel
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Transfer Learning

Named entity recognition (NER) is an important task that constitutes the basis for multiple downstream natural language processing tasks. Traditional machine learning approaches for NER rely on annotated corpora. However, these are only largely available for standard domains, e.g., news articles. Domain-specific NER often lacks annotated training data and therefore two options are of interest: expensive manual annotations or transfer learning. In this paper, we study a selection of cross-domain NER models and evaluate them for use in the art domain, particularly for recognizing artwork titles in digitized art-historic documents. For the evaluation of the models, we employ a variety of source domain datasets and analyze how each source domain dataset impacts the performance of the different models for our target domain. Additionally, we analyze the impact of the source domain’s entity types, looking for a better understanding of how the transfer learning models adapt different source entity types into our target entity types.
An Exploratory Study on Temporally Evolving Discussion around Covid-19 using Diachronic Word Embeddings
Authors: Avinash Tulasi, Asanobu Kitamoto, Ponnurangam Kumaraguru, Arun Balaji Buduru
Date: 2021-12-01 00:00:00
Proceeding: NLP4DH (ICON) 2021 12
Tasks: Diachronic Word Embeddings, Word Embeddings

Covid 19 has seen the world go into a lock down and unconventional social situations throughout. During this time, the world saw a surge in information sharing around the pandemic and the topics shared in the time were diverse. People’s sentiments have changed during this period. Given the wide spread usage of Online Social Networks (OSN) and support groups, the user sentiment is well reflected in online discussions. In this work, we aim to show the topics under discussion, evolution of discussions, change in user sentiment during the pandemic. Alongside which, we also demonstrate the possibility of exploratory analysis to find pressing topics, change in perception towards the topics and ways to use the knowledge extracted from online discussions. For our work we employ Diachronic Word embeddings which capture the change in word usage over time. With the help of analysis from temporal word usages, we show the change in people’s option on covid-19 from being a conspiracy, to the post-covid topics that surround vaccination.
Constrained Decoding for Technical Term Retention in English-Hindi MT
Authors: Niyati Bafna, Martin Vastl, Ondřej Bojar
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Machine Translation, Sentence, Translation

Technical terms may require special handling when the target audience is bilingual, depending on the cultural and educational norms of the society in question. In particular, certain translation scenarios may require “term retention” i.e. preserving of the source language technical terms in the target language output to produce a fluent and comprehensible code-switched sentence. We show that a standard transformer-based machine translation model can be adapted easily to perform this task with little or no damage to the general quality of its output. We present an English-to-Hindi model that is trained to obey a “retain” signal, i.e. it can perform the required code-mixing on a list of terms, possibly unseen, provided at runtime. We perform automatic evaluation using BLEU as well as F1 metrics on the list of retained terms; we also collect manual judgments on the quality of the output sentences.
Named Entity-Factored Transformer for Proper Noun Translation
Authors: Kohichi Takai, Gen Hattori, Akio Yoneyama, Keiji Yasuda, Katsuhito Sudoh, Satoshi Nakamura
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, NMT, Proper Noun, Translation

Subword-based neural machine translation decreases the number of out-of-vocabulary (OOV) words and also keeps the translation quality if input sentences include OOV words. The subword-based NMT decomposes a word into shorter units to solve the OOV problem, but it does not work well for non-compositional proper nouns due to the construction of the shorter unit from words. Furthermore, the lack of translation also occurs in proper noun translation. The proposed method applies the Named Entity (NE) fea-ture vector to Factored Transformer for accurate proper noun translation. The proposed method uses two features which are input sentences in subwords unit and the feature obtained from Named Entity Recognition (NER). The pro-posed method improves the problem of non-compositional proper nouns translation included a low-frequency word. According to the experiments, the proposed method using the best NE feature vector outperformed the baseline sub-word-based transformer model by more than 9.6 points in proper noun accuracy and 2.5 points in the BLEU score.
Multi-Task Learning for Improving Gender Accuracy in Neural Machine Translation
Authors: Carlos Escolano, Graciela Ojeda, Christine Basta, Marta R. Costa-Jussa
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Machine Translation, Multi-Task Learning, Translation

Machine Translation is highly impacted by social biases present in data sets, indicating that it reflects and amplifies stereotypes. In this work, we study mitigating gender bias by jointly learning the translation, the part-of-speech, and the gender of the target language with different morphological complexity. This approach has shown improvements up to 6.8 points in gender accuracy without significantly impacting the translation quality.
lakṣyārtha (Indicated Meaning) of Śabdavyāpāra (Function of a Word) framework from kāvyaśāstra (The Science of Literary Studies) in Samskṛtam : Its application to Literary Machine Translation and other NLP tasks
Authors: Sripathi Sripada, Anupama Ryali, Raghuram Sheshadri
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Machine Translation, Sentence, Translation

A key challenge in Literary Machine Translation is that the meaning of a sentence can be different from the sum of meanings of all the words it possesses. This poses the problem of requiring large amounts of consistently labelled training data across a variety of usages and languages. In this paper, we propose that we can economically train machine translation models to identify and paraphrase such sentences by leveraging the language independent framework of Śabdavyāpāra (Function of a Word), from Literary Sciences in Saṃskṛtam, and its definition of lakṣyārtha (‘Indicated’ meaning). An Indicated meaning exists where there is incompatibility among the literal meanings of the words in a sentence (irrespective of language). The framework defines seven categories of Indicated meaning and their characteristics. As a pilot, we identified 300 such sentences from literary and regular usage, labelled them and trained a 2d Convolutional Neural Network to categorise a sentence based on the category of Indicated meaning and finetuned a T5 to paraphrase them. We compared these paraphrased sentences with those paraphrased by a T5 finetuned on Quora Paraphrase dataset of 400,000 sentence pairs. The T5 finetuned on the Indicated meaning examples performed consistently better. Moreover, a Google Translate translates these paraphrased sentences accurately and consistently across languages
EduMT: Developing Machine Translation System for Educational Content in Indian Languages
Authors: Ramakrishna Appicharla, Asif Ekbal, Pushpak Bhattacharyya
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Data Augmentation, Domain Adaptation, Machine Translation, NMT, Translation

In this paper, we explore various approaches to build Hindi to Bengali Neural Machine Translation (NMT) systems for the educational domain. Translation of educational content poses several challenges, such as unavailability of gold standard data for model building, extensive uses of domain-specific terms, as well as the presence of noise in the form of spontaneous speech as the corpus is prepared from subtitle data and noise due to the process of corpus creation through back-translation. We create an educational parallel corpus by crawling lecture subtitles and translating them into Hindi and Bengali using Google translate. We also create a clean parallel corpus by post-editing synthetic corpus via annotation and crowd-sourcing. We build NMT systems on the prepared corpus with domain adaptation objectives. We also explore data augmentation methods by automatically cleaning synthetic corpus and using it to further train the models. We experiment with combining domain adaptation objective with multilingual NMT. We report BLEU and TER scores of all the models on a manually created Hindi-Bengali educational testset. Our experiments show that the multilingual domain adaptation model outperforms all the other models by achieving 34.8 BLEU and 0.466 TER scores.
An Experiment on Speech-to-Text Translation Systems for Manipuri to English on Low Resource Setting
Authors: Loitongbam Sanayai Meetei, Laishram Rahul, Alok Singh, Salam Michael Singh, Thoudam Doren Singh, Sivaji Bandyopadhyay
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, Speech-to-Text, Speech-to-Text Translation, Translation

In this paper, we report the experimental findings of building Speech-to-Text translation systems for Manipuri-English on low resource setting which is first of its kind in this language pair. For this purpose, a new dataset consisting of a Manipuri-English parallel corpus along with the corresponding audio version of the Manipuri text is built. Based on this dataset, a benchmark evaluation is reported for the Manipuri-English Speech-to-Text translation using two approaches: 1) a pipeline model consisting of ASR (Automatic Speech Recognition) and Machine translation, and 2) an end-to-end Speech-to-Text translation. Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and Time delay neural network (TDNN) Acoustic models are used to build two different pipeline systems using a shared MT system. Experimental result shows that the TDNN model outperforms GMM-HMM model significantly by a margin of 2.53% WER. However, their evaluation of Speech-to-Text translation differs by a small margin of 0.1 BLEU. Both the pipeline translation models outperform the end-to-end translation model by a margin of 2.6 BLEU score.
On the Transferability of Massively Multilingual Pretrained Models in the Pretext of the Indo-Aryan and Tibeto-Burman Languages
Authors: Salam Michael Singh, Loitongbam Sanayai Meetei, Alok Singh, Thoudam Doren Singh, Sivaji Bandyopadhyay
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Machine Translation, Transfer Learning, Translation

In recent times, machine translation models can learn to perform implicit bridging between language pairs never seen explicitly during training and showing that transfer learning helps for languages with constrained resources. This work investigates the low resource machine translation via transfer learning from multilingual pre-trained models i.e. mBART-50 and mT5-base in the pretext of Indo-Aryan (Assamese and Bengali) and Tibeto-Burman (Manipuri) languages via finetuning as a downstream task. Assamese and Manipuri were absent in the pretraining of both mBART-50 and the mT5 models. However, the experimental results attest that the finetuning from these pre-trained models surpasses the multilingual model trained from scratch.
Generating Slogans with Linguistic Features using Sequence-to-Sequence Transformer
Authors: Yeoun Yi, Hyopil Shin
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: POS

Previous work generating slogans depended on templates or summaries of company descriptions, making it difficult to generate slogans with linguistic features. We present LexPOS, a sequence-to-sequence transformer model that generates slogans given phonetic and structural information. Our model searches for phonetically similar words given user keywords. Both the sound-alike words and user keywords become lexical constraints for generation. For structural repetition, we use POS constraints. Users can specify any repeated phrase structure by POS tags. Our model-generated slogans are more relevant to the original slogans than those of baseline models. They also show phonetic and structural repetition during inference, representative features of memorable slogans.
Stylistic MR-to-Text Generation Using Pre-trained Language Models
Authors: Kunal Pagarey, Kanika Kalra, Abhay Garg, Saumajit Saha, Mayur Patidar, Shirish Karande
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Decoder, POS, Sentence, Text Generation

We explore the ability of pre-trained language models BART, an encoder-decoder model, GPT2 and GPT-Neo, both decoder-only models for generating sentences from structured MR tags as input. We observe best results on several metrics for the YelpNLG and E2E datasets. Style based implicit tags such as emotion, sentiment, length etc., allows for controlled generation but it is typically not present in MR. We present an analysis on YelpNLG showing BART can express the content with stylistic variations in the structure of the sentence. Motivated with the results, we define a new task of emotional situation generation from various POS tags and emotion label values as MR using EmpatheticDialogues dataset and report a baseline. Encoder-Decoder attention analysis shows that BART learns different aspects in MR at various layers and heads.
Deep Learning Based Approach For Detecting Suicidal Ideation in Hindi-English Code-Mixed Text: Baseline and Corpus
Authors: Kaustubh Agarwal, Bhavya Dhingra
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12

Suicide rates are rising among the youth, and the high association with suicidal ideation expression on social media necessitates further research into models for detecting suicidal ideation in text, such as tweets, to enable mitigation. Existing research has proven the feasibility of detecting suicidal ideation on social media in a particular language. However, studies have shown that bilingual and multilingual speakers tend to use code-mixed text on social media making the detection of suicidal ideation on code-mixed data crucial, even more so with the increasing number of bilingual and multilingual speakers. In this study we create a code-mixed Hindi-English (Hinglish) dataset for detection of suicidal ideation and evaluate the performance of traditional classifiers, deep learning architectures, and transformers on it. Among the tested classifier architectures, Indic BERT gave the best results with an accuracy of 98.54%.
Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques
Authors: Ratnesh Joshi, Arindam Chatterjee, Asif Ekbal
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: intent-classification, Intent Classification, Intent Detection, Natural Language Understanding

Deep learning based methods have shown tremendous success in several Natural Language Processing (NLP) tasks. The recent trends in the usage of Deep Learning based models for natural language tasks have definitely produced incredible performance for several application areas. However, one major problem that most of these models face is the lack of transparency, i.e. the actual decision process of the underlying model is not explainable. In this paper, at first we solve a very fundamental problem of Natural Language Understanding (NLU), i.e. intent detection using a Bi-directional Long Short Term Memory (BiLSTM). In order to determine the defining features that lead to a specific intent class, we use the Layerwise Relevance Propagation (LRP) algorithm to find the defining feature(s). In the process, we conclude that saliency method of eLRP (epsilon Layerwise Relevance Propagation) is a prominent process for highlighting the important features of the input responsible for the current classification which results in significant insights to the inner workings, such as the reasons for misclassification by the black box model.
MRE : Multi Relationship Extractor for Persona based Empathetic Conversational Model
Authors: Bharatram Natarajan, Abhijit Nargund
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12

Artificial intelligence(AI) has come a long way in aiding the user requirements in many fields and domains. However, the current AI systems do not generate human- like response for user query. Research in these areas have started gaining traction recently with explorations on persona or empathy based response selection. But the combination of both the parameters in an open domain haven’t been explored in detail by the research community. The current work highlights the effect of persona on empathetic response. This research paper concentrates on improving the response selection model for PEC dataset, containing both persona information and empathetic response. This is achieved using an enhanced multi relationship extractor and phrase based information for the selection of response.
An End-to-End Speech Recognition for the Nepali Language
Authors: Sunil Regmi, Bal Krishna Bal
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Decoder, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In this era of AI and Deep Learning, Speech Recognition has achieved fairly good levels of accuracy and is bound to change the way humans interact with computers, which happens mostly through texts today. Most of the speech recognition systems for the Nepali language to date use conventional approaches which involve separately trained acoustic, pronunciation and language model components. Creating a pronunciation lexicon from scratch and defining phoneme sets for the language requires expert knowledge, and at the same time is time-consuming. In this work, we present an End-to-End ASR approach, which uses a joint CTC- attention-based encoder-decoder and a Recurrent Neural Network based language modeling which eliminates the need of creating a pronunciation lexicon from scratch. ESPnet toolkit which uses Kaldi Style of data preparation is the framework used for this work. The speech and transcription data used for this research is freely available on the Open Speech and Language Resources (OpenSLR). We use about 159k transcribed speech data to train the speech recognition model which currently recognizes speech input with the CER of 10.3%.
Impact of Microphone position Measurement Error on Multi Channel Distant Speech Recognition & Intelligibility
Authors: Karan Nathwani, Sunil Kumar Kopparapu
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Distant Speech Recognition, Position, Room Impulse Response (RIR), speech-recognition, Speech Recognition, Speech-to-Text

It was shown in (Raikar et al., 2020) that the measurement error in the microphone position affected the room impulse response (RIR) which in turn affected the single channel speech recognition. In this paper, we ex-tend this to study the more complex and realistic scenario of multi channel distant speech recognition. Specifically we simulate m speakers in a given room with n microphones speaking without overlap. Then channel audio is beamformed and passed through a speech to text (s2t) engine. We compare the s2t accuracy when the microphone locations are known exactly (ground truth) with the s2t accuracy when there is a measurement error in the location of the microphone. We report the performance of an end-to-end s2t on beamformed input in terms of character error rate (CER) and and also speech intelligibility and quality in terms of STOI and PESQ respectively.
IE-CPS Lexicon: An Automatic Speech Recognition Oriented Indian-English Pronunciation Dictionary
Authors: Shelly Jain, Aditya Yadavalli, Ganesh Mirishkar, Chiranjeevi Yarra, Anil Kumar Vuppala
Date: 2021-12-01 00:00:00
Proceeding: ICON 2021 12
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Indian English (IE), on the surface, seems quite similar to standard English. However, closer observation shows that it has actually been influenced by the surrounding vernacular languages at several levels from phonology to vocabulary and syntax. Due to this, automatic speech recognition (ASR) systems developed for American or British varieties of English result in poor performance on Indian English data. The most prominent feature of Indian English is the characteristic pronunciation of the speakers. The systems are unable to learn these acoustic variations while modelling and cannot parse the non-standard articulation of non-native speakers. For this purpose, we propose a new phone dictionary developed based on the Indian language Common Phone Set (CPS). The dictionary maps the phone set of American English to existing Indian phones based on perceptual similarity. This dictionary is named Indian English Common Phone Set (IE-CPS). Using this, we build an Indian English ASR system and compare its performance with an American English ASR system on speech data of both varieties of English. Our experiments on the IE-CPS show that it is quite effective at modelling the pronunciation of the average speaker of Indian English. ASR systems trained on Indian English data perform much better when modelled using IE-CPS, achieving a reduction in the word error rate (WER) of upto 3.95% when used in place of CMUdict. This shows the need for a different lexicon for Indian English.