pwc-archive/papers-with-abstracts huggingface dataset:
H-AES: Towards Automated Essay Scoring for Hindi
Authors: Shubhankar Singh, Anirudh Pupneja, Shivaansh Mital, Cheril Shah, Manish Bawkar, Lakshman Prasad Gupta, Ajit Kumar, Yaman Kumar, Rushali Gupta, Rajiv Ratn Shah
Date: 2023-02-28 00:00:00
Tasks: Automated Essay Scoring

The use of Natural Language Processing (NLP) for Automated Essay Scoring (AES) has been well explored in the English language, with benchmark models exhibiting performance comparable to human scorers. However, AES in Hindi and other low-resource languages remains unexplored. In this study, we reproduce and compare state-of-the-art methods for AES in the Hindi domain. We employ classical feature-based Machine Learning (ML) and advanced end-to-end models, including LSTM Networks and Fine-Tuned Transformer Architecture, in our approach and derive results comparable to those in the English language domain. Hindi being a low-resource language, lacks a dedicated essay-scoring corpus. We train and evaluate our models using translated English essays and empirically measure their performance on our own small-scale, real-world Hindi corpus. We follow this up with an in-depth analysis discussing prompt-specific behavior of different language models implemented.
Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face
Authors: Christopher Akiki, Odunayo Ogundepo, Aleksandra Piktus, Xinyu Zhang, Akintunde Oladipo, Jimmy Lin, Martin Potthast
Date: 2023-02-28 00:00:00
Tasks: Information Retrieval, Retrieval

We present Spacerini, a tool that integrates the Pyserini toolkit for reproducible information retrieval research with Hugging Face to enable the seamless construction and deployment of interactive search engines. Spacerini makes state-of-the-art sparse and dense retrieval models more accessible to non-IR practitioners while minimizing deployment effort. This is useful for NLP researchers who want to better understand and validate their research by performing qualitative analyses of training corpora, for IR researchers who want to demonstrate new retrieval models integrated into the growing Pyserini ecosystem, and for third parties reproducing the work of other researchers. Spacerini is open source and includes utilities for loading, preprocessing, indexing, and deploying search engines locally and remotely. We demonstrate a portfolio of 13 search engines created with Spacerini for different use cases.
Automatic Heteronym Resolution Pipeline Using RAD-TTS Aligners
Authors: Jocelyn Huang, Evelina Bakhturina, Oktai Tatanov
Date: 2023-02-28 00:00:00
Tasks: text-to-speech, Text to Speech

Grapheme-to-phoneme (G2P) transduction is part of the standard text-to-speech (TTS) pipeline. However, G2P conversion is difficult for languages that contain heteronyms -- words that have one spelling but can be pronounced in multiple ways. G2P datasets with annotated heteronyms are limited in size and expensive to create, as human labeling remains the primary method for heteronym disambiguation. We propose a RAD-TTS Aligner-based pipeline to automatically disambiguate heteronyms in datasets that contain both audio with text transcripts. The best pronunciation can be chosen by generating all possible candidates for each heteronym and scoring them with an Aligner model. The resulting labels can be used to create training datasets for use in both multi-stage and end-to-end G2P systems.
Large Language Models Are State-of-the-Art Evaluators of Translation Quality
Authors: Tom Kocmi, Christian Federmann
Date: 2023-02-28 00:00:00
Tasks: Translation, valid

We describe GEMBA, a GPT-based metric for assessment of translation quality, which works both with a reference translation and without. In our evaluation, we focus on zero-shot prompting, comparing four prompt variants in two modes, based on the availability of the reference. We investigate nine versions of GPT models, including ChatGPT and GPT-4. We show that our method for translation quality assessment only works with GPT~3.5 and larger models. Comparing to results from WMT22's Metrics shared task, our method achieves state-of-the-art accuracy in both modes when compared to MQM-based human labels. Our results are valid on the system level for all three WMT22 Metrics shared task language pairs, namely English into German, English into Russian, and Chinese into English. This provides a first glimpse into the usefulness of pre-trained, generative large language models for quality assessment of translations. We publicly release all our code and prompt templates used for the experiments described in this work, as well as all corresponding scoring results, to allow for external validation and reproducibility.
A Survey on Long Text Modeling with Transformers
Authors: Zican Dong, Tianyi Tang, Lunyi Li, Wayne Xin Zhao
Date: 2023-02-28 00:00:00
Tasks: Survey

Modeling long texts has been an essential technique in the field of natural language processing (NLP). With the ever-growing number of long documents, it is important to develop effective modeling methods that can process and analyze such texts. However, long texts pose important research challenges for existing text models, with more complex semantics and special characteristics. In this paper, we provide an overview of the recent advances on long texts modeling based on Transformer models. Firstly, we introduce the formal definition of long text modeling. Then, as the core content, we discuss how to process long input to satisfy the length limitation and design improved Transformer architectures to effectively extend the maximum context length. Following this, we discuss how to adapt Transformer models to capture the special characteristics of long texts. Finally, we describe four typical applications involving long text modeling and conclude this paper with a discussion of future directions. Our survey intends to provide researchers with a synthesis and pointer to related work on long text modeling.
Text classification dataset and analysis for Uzbek language
Authors: Elmurod Kuriyozov, Ulugbek Salaev, Sanatbek Matlatipov, Gayrat Matlatipov
Date: 2023-02-28 00:00:00
Tasks: Classification, text-classification, Text Classification

Text classification is an important task in Natural Language Processing (NLP), where the goal is to categorize text data into predefined classes. In this study, we analyse the dataset creation steps and evaluation techniques of multi-label news categorisation task as part of text classification. We first present a newly obtained dataset for Uzbek text classification, which was collected from 10 different news and press websites and covers 15 categories of news, press and law texts. We also present a comprehensive evaluation of different models, ranging from traditional bag-of-words models to deep learning architectures, on this newly created dataset. Our experiments show that the Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) based models outperform the rule-based models. The best performance is achieved by the BERTbek model, which is a transformer-based BERT model trained on the Uzbek corpus. Our findings provide a good baseline for further research in Uzbek text classification.
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases
Authors: Yanchen Liu, Jing Yan, Yan Chen, Jing Liu, Hua Wu
Date: 2023-02-28 00:00:00
Tasks: Adversarial Robustness, Natural Language Inference, Paraphrase Identification

Recent studies reveal that various biases exist in different NLP tasks, and over-reliance on biases results in models' poor generalization ability and low adversarial robustness. To mitigate datasets biases, previous works propose lots of debiasing techniques to tackle specific biases, which perform well on respective adversarial sets but fail to mitigate other biases. In this paper, we propose a new debiasing method Sparse Mixture-of-Adapters (SMoA), which can mitigate multiple dataset biases effectively and efficiently. Experiments on Natural Language Inference and Paraphrase Identification tasks demonstrate that SMoA outperforms full-finetuning, adapter tuning baselines, and prior strong debiasing methods. Further analysis indicates the interpretability of SMoA that sub-adapter can capture specific pattern from the training data and specialize to handle specific bias.
Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw Dataset
Authors: Brielen Madureira, David Schlangen
Date: 2023-02-28 00:00:00
Tasks: Instruction Following, visual instruction following

In visual instruction-following dialogue games, players can engage in repair mechanisms in face of an ambiguous or underspecified instruction that cannot be fully mapped to actions in the world. In this work, we annotate Instruction Clarification Requests (iCRs) in CoDraw, an existing dataset of interactions in a multimodal collaborative dialogue game. We show that it contains lexically and semantically diverse iCRs being produced self-motivatedly by players deciding to clarify in order to solve the task successfully. With 8.8k iCRs found in 9.9k dialogues, CoDraw-iCR (v1) is a large spontaneous iCR corpus, making it a valuable resource for data-driven research on clarification in dialogue. We then formalise and provide baseline models for two tasks: Determining when to make an iCR and how to recognise them, in order to investigate to what extent these tasks are learnable from data.
Information-Restricted Neural Language Models Reveal Different Brain Regions' Sensitivity to Semantics, Syntax and Context
Authors: Alexandre Pasquiou, Yair Lakretz, Bertrand Thirion, Christophe Pallier
Date: 2023-02-28 00:00:00
Tasks: Language Modeling, Language Modelling, Sensitivity, Sentence

A fundamental question in neurolinguistics concerns the brain regions involved in syntactic and semantic processing during speech comprehension, both at the lexical (word processing) and supra-lexical levels (sentence and discourse processing). To what extent are these regions separated or intertwined? To address this question, we trained a lexical language model, Glove, and a supra-lexical language model, GPT-2, on a text corpus from which we selectively removed either syntactic or semantic information. We then assessed to what extent these information-restricted models were able to predict the time-courses of fMRI signal of humans listening to naturalistic text. We also manipulated the size of contextual information provided to GPT-2 in order to determine the windows of integration of brain regions involved in supra-lexical processing. Our analyses show that, while most brain regions involved in language are sensitive to both syntactic and semantic variables, the relative magnitudes of these effects vary a lot across these regions. Furthermore, we found an asymmetry between the left and right hemispheres, with semantic and syntactic processing being more dissociated in the left hemisphere than in the right, and the left and right hemispheres showing respectively greater sensitivity to short and long contexts. The use of information-restricted NLP models thus shed new light on the spatial organization of syntactic processing, semantic processing and compositionality.
HugNLP: A Unified and Comprehensive Library for Natural Language Processing
Authors: Jianing Wang, Nuo Chen, Qiushi Sun, Wenkang Huang, Chengyu Wang, Ming Gao
Date: 2023-02-28 00:00:00

In this paper, we introduce HugNLP, a unified and comprehensive library for natural language processing (NLP) with the prevalent backend of HuggingFace Transformers, which is designed for NLP researchers to easily utilize off-the-shelf algorithms and develop novel methods with user-defined models and tasks in real-world scenarios. HugNLP consists of a hierarchical structure including models, processors and applications that unifies the learning process of pre-trained language models (PLMs) on different NLP tasks. Additionally, we present some featured NLP applications to show the effectiveness of HugNLP, such as knowledge-enhanced PLMs, universal information extraction, low-resource mining, and code understanding and generation, etc. The source code will be released on GitHub (https://github.com/wjn1996/HugNLP).
Augmented Transformers with Adaptive n-grams Embedding for Multilingual Scene Text Recognition
Authors: Xueming Yan, Zhihang Fang, Yaochu Jin
Date: 2023-02-28 00:00:00
Tasks: Language Identification, Scene Text Recognition

While vision transformers have been highly successful in improving the performance in image-based tasks, not much work has been reported on applying transformers to multilingual scene text recognition due to the complexities in the visual appearance of multilingual texts. To fill the gap, this paper proposes an augmented transformer architecture with n-grams embedding and cross-language rectification (TANGER). TANGER consists of a primary transformer with single patch embeddings of visual images, and a supplementary transformer with adaptive n-grams embeddings that aims to flexibly explore the potential correlations between neighbouring visual patches, which is essential for feature extraction from multilingual scene texts. Cross-language rectification is achieved with a loss function that takes into account both language identification and contextual coherence scoring. Extensive comparative studies are conducted on four widely used benchmark datasets as well as a new multilingual scene text dataset containing Indonesian, English, and Chinese collected from tourism scenes in Indonesia. Our experimental results demonstrate that TANGER is considerably better compared to the state-of-the-art, especially in handling complex multilingual scene texts.
Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation
Authors: Lukas Edman, Gabriele Sarti, Antonio Toral, Gertjan van Noord, Arianna Bisazza
Date: 2023-02-28 00:00:00
Tasks: Machine Translation, NMT, Translation

Pretrained character-level and byte-level language models have been shown to be competitive with popular subword models across a range of Natural Language Processing (NLP) tasks. However, there has been little research on their effectiveness for neural machine translation (NMT), particularly within the popular pretrain-then-finetune paradigm. This work performs an extensive comparison across multiple languages and experimental conditions of character- and subword-level pretrained models (ByT5 and mT5, respectively) on NMT. We show the effectiveness of character-level modeling in translation, particularly in cases where fine-tuning data is limited. In our analysis, we show how character models' gains in translation quality are reflected in better translations of orthographically similar words and rare words. While evaluating the importance of source texts in driving model predictions, we highlight word-level patterns within ByT5, suggesting an ability to modulate word-level and character-level information during generation. We conclude by assessing the efficiency tradeoff of byte models, suggesting their usage in non-time-critical scenarios to boost translation quality.
TabGenie: A Toolkit for Table-to-Text Generation
Authors: Zdeněk Kasner, Ekaterina Garanina, Ondřej Plátek, Ondřej Dušek
Date: 2023-02-27 00:00:00
Tasks: Data-to-Text Generation, Table-to-Text Generation, Text Generation

Heterogenity of data-to-text generation datasets limits the research on data-to-text generation systems. We present TabGenie - a toolkit which enables researchers to explore, preprocess, and analyze a variety of data-to-text generation datasets through the unified framework of table-to-text generation. In TabGenie, all the inputs are represented as tables with associated metadata. The tables can be explored through the web interface, which also provides an interactive mode for debugging table-to-text generation, facilitates side-by-side comparison of generated system outputs, and allows easy exports for manual analysis. Furthermore, TabGenie is equipped with command line processing tools and Python bindings for unified dataset loading and processing. We release TabGenie as a PyPI package and provide its open-source code and a live demo at https://github.com/kasnerz/tabgenie.
Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and Understanding
Authors: Yifan Peng, Kwangyoun Kim, Felix Wu, Prashant Sridhar, Shinji Watanabe
Date: 2023-02-27 00:00:00
Tasks: Model Compression, Representation Learning, speech-recognition, Speech Recognition, Speech Representation Learning

Self-supervised speech representation learning (SSL) has shown to be effective in various downstream tasks, but SSL models are usually large and slow. Model compression techniques such as pruning aim to reduce the model size and computation without degradation in accuracy. Prior studies focus on the pruning of Transformers; however, speech models not only utilize a stack of Transformer blocks, but also combine a frontend network based on multiple convolutional layers for low-level feature representation learning. This frontend has a small size but a heavy computational cost. In this work, we propose three task-specific structured pruning methods to deal with such heterogeneous networks. Experiments on LibriSpeech and SLURP show that the proposed method is more accurate than the original wav2vec2-base with 10% to 30% less computation, and is able to reduce the computation by 40% to 50% without any degradation.
SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processing
Authors: Weidong Chen, Xiaofen Xing, Xiangmin Xu, Jianxin Pang, Lan Du
Date: 2023-02-27 00:00:00
Tasks: Alzheimer's Disease Detection, Emotion Recognition, Speech Emotion Recognition

Paralinguistic speech processing is important in addressing many issues, such as sentiment and neurocognitive disorder analyses. Recently, Transformer has achieved remarkable success in the natural language processing field and has demonstrated its adaptation to speech. However, previous works on Transformer in the speech field have not incorporated the properties of speech, leaving the full potential of Transformer unexplored. In this paper, we consider the characteristics of speech and propose a general structure-based framework, called SpeechFormer++, for paralinguistic speech processing. More concretely, following the component relationship in the speech signal, we design a unit encoder to model the intra- and inter-unit information (i.e., frames, phones, and words) efficiently. According to the hierarchical relationship, we utilize merging blocks to generate features at different granularities, which is consistent with the structural pattern in the speech signal. Moreover, a word encoder is introduced to integrate word-grained features into each unit encoder, which effectively balances fine-grained and coarse-grained information. SpeechFormer++ is evaluated on the speech emotion recognition (IEMOCAP & MELD), depression classification (DAIC-WOZ) and Alzheimer's disease detection (Pitt) tasks. The results show that SpeechFormer++ outperforms the standard Transformer while greatly reducing the computational cost. Furthermore, it delivers superior results compared to the state-of-the-art approaches.
Estimation-of-Distribution Algorithms for Multi-Valued Decision Variables
Authors: Firas Ben Jedidia, Benjamin Doerr, Martin S. Krejca
Date: 2023-02-28 00:00:00

The majority of research on estimation-of-distribution algorithms (EDAs) concentrates on pseudo-Boolean optimization and permutation problems, leaving the domain of EDAs for problems in which the decision variables can take more than two values, but which are not permutation problems, mostly unexplored. To render this domain more accessible, we propose a natural way to extend the known univariate EDAs to this setting. Different from a naive reduction to the binary case, our approach avoids additional constraints. Since understanding genetic drift is crucial for an optimal parameter choice, we extend the known quantitative analysis of genetic drift to EDAs for multi-valued variables. Roughly speaking, when the variables take $r$ different values, the time for genetic drift to become significant is $r$ times shorter than in the binary case. Consequently, the update strength of the probabilistic model has to be chosen $r$ times lower now. To investigate how desired model updates take place in this framework, we undertake a mathematical runtime analysis on the $r$-valued \leadingones problem. We prove that with the right parameters, the multi-valued UMDA solves this problem efficiently in $O(r\ln(r)^2 n^2 \ln(n))$ function evaluations. This bound is nearly tight as our lower bound $\Omega(r\ln(r) n^2 \ln(n))$ shows. Overall, our work shows that our good understanding of binary EDAs naturally extends to the multi-valued setting, and it gives advice on how to set the main parameters of multi-values EDAs.
Transitions between quasi-stationary states in traffic systems: Cologne orbital motorways as an example
Authors: Shanshan Wang, Michael Schreckenberg, Thomas Guhr
Date: 2023-02-28 00:00:00

Traffic systems can operate in different modes. In a previous work, we identified these modes as different quasi-stationary states in the correlation structure. Here, we analyze the transitions between such quasi-stationary states, i.e., how the system changes its operational mode. In the longer run this might be helpful to forecast the time evolution of correlation patterns in traffic. We take Cologne orbital motorways as an example, we construct a state transition network for each quarter of 2015 and find a seasonal dependence for those quasi-stationary states in the traffic system. Using the PageRank algorithm, we identify and explore the dominant states which occur frequently within a moving time window of 60 days in 2015. To the best of our knowledge, this is the first study of this type for traffic systems.
Maximum Likelihood With a Time Varying Parameter
Authors: Alberto Lanconelli, Christopher S. A. Lauria
Date: 2023-02-28 00:00:00

We consider the problem of tracking an unknown time varying parameter that characterizes the probabilistic evolution of a sequence of independent observations. To this aim, we propose a stochastic gradient descent-based recursive scheme in which the log-likelihood of the observations acts as time varying gain function. We prove convergence in mean-square error in a suitable neighbourhood of the unknown time varying parameter and illustrate the details of our findings in the case where data are generated from distributions belonging to the exponential family.
Extending English IR methods to multi-lingual IR
Authors: Carlos Lassance
Date: 2023-02-28 00:00:00
Tasks: Document Translation, Reranking

This paper describes our participation in the 2023 WSDM CUP - MIRACL challenge. Via a combination of i) document translation; ii) multilingual SPLADE and Contriever; and iii) multilingual RankT5 and many other models, we were able to get first place in both the known and surprise languages tracks. Our strategy mostly revolved around getting the most diverse runs for the first stage and then throwing all possible reranking techniques. While this was not a first for many techniques, we had some things that we believe were never tried before, for example, we train the first SPLADE model that is effectively capable of working in more than 10 languages. However, a more careful study of the results is needed in order to verify if we were able to get first place just due to brute force or if the hybrids we developed really brought improvements over the other team's solutions.