pwc-archive/papers-with-abstracts huggingface dataset:
A Circular-Structured Representation for Visual Emotion Distribution Learning
Authors: Jingyuan Yang, Jie Li, Leida Li, Xiumei Wang, Xinbo Gao
Date: 2021-06-23 00:00:00
Proceeding: CVPR 2021 1
Tasks: Emotion Recognition

Visual Emotion Analysis (VEA) has attracted increasing attention recently with the prevalence of sharing images on social networks. Since human emotions are ambiguous and subjective, it is more reasonable to address VEA in a label distribution learning (LDL) paradigm rather than a single-label classification task. Different from other LDL tasks, there exist intrinsic relationships between emotions and unique characteristics within them, as demonstrated in psychological theories. Inspired by this, we propose a well-grounded circular-structured representation to utilize the prior knowledge for visual emotion distribution learning. To be specific, we first construct an Emotion Circle to unify any emotional state within it. On the proposed Emotion Circle, each emotion distribution is represented with an emotion vector, which is defined with three attributes (i.e., emotion polarity, emotion type, emotion intensity) as well as two properties (i.e., similarity, additivity). Besides, we design a novel Progressive Circular (PC) loss to penalize the dissimilarities between predicted emotion vector and labeled one in a coarse-to-fine manner, which further boosts the learning process in an emotion-specific way. Extensive experiments and comparisons are conducted on public visual emotion distribution datasets, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.
A Graph-based Method for Session-based Recommendations
Authors: Marina Delianidi, Michail Salampasis, Konstantinos Diamantaras, Theodosios Siomos, Alkiviadis Katsalis, Iphigenia Karaveli
Date: 2021-06-22 00:00:00
Tasks: Management, Session-Based Recommendations

We present a graph-based approach for the data management tasks and the efficient operation of a system for session-based next-item recommendations. The proposed method can collect data continuously and incrementally from an ecommerce web site, thus seemingly prepare the necessary data infrastructure for the recommendation algorithm to operate without any excessive training phase. Our work aims at developing a recommender method that represents a balance between data processing and management efficiency requirements and the effectiveness of the recommendations produced. We use the Neo4j graph database to implement a prototype of such a system. Furthermore, we use an industry dataset corresponding to a typical e-commerce session-based scenario, and we report on experiments using our graph-based approach and other state-of-the-art machine learning and deep learning methods.
Improving Transformer-based Sequential Recommenders through Preference Editing
Authors: Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Jun Ma, Maarten de Rijke
Date: 2021-06-23 00:00:00
Tasks: Recommendation Systems, Self-Supervised Learning, Sequential Recommendation

One of the key challenges in Sequential Recommendation (SR) is how to extract and represent user preferences. Traditional SR methods rely on the next item as the supervision signal to guide preference extraction and representation. We propose a novel learning strategy, named preference editing. The idea is to force the SR model to discriminate the common and unique preferences in different sequences of interactions between users and the recommender system. By doing so, the SR model is able to learn how to identify common and unique user preferences, and thereby do better user preference extraction and representation. We propose a transformer based SR model, named MrTransformer (Multi-preference Transformer), that concatenates some special tokens in front of the sequence to represent multiple user preferences and makes sure they capture different aspects through a preference coverage mechanism. Then, we devise a preference editing-based self-supervised learning mechanism for training MrTransformer which contains two main operations: preference separation and preference recombination. The former separates the common and unique user preferences for a given pair of sequences. The latter swaps the common preferences to obtain recombined user preferences for each sequence. Based on the preference separation and preference recombination operations, we define two types of SSL loss that require that the recombined preferences are similar to the original ones, and the common preferences are close to each other. We carry out extensive experiments on two benchmark datasets. MrTransformer with preference editing significantly outperforms state-of-the-art SR methods in terms of Recall, MRR and NDCG. We find that long sequences whose user preferences are harder to extract and represent benefit most from preference editing.
Extractive Explanations for Interpretable Text Ranking
Authors: Jurek Leonhardt, Koustav Rudra, Avishek Anand
Date: 2021-06-23 00:00:00
Tasks: Document Ranking, Retrieval, Sentence

Neural document ranking models perform impressively well due to superior language understanding gained from pre-training tasks. However, due to their complexity and large number of parameters, these (typically transformer-based) models are often non-interpretable in that ranking decisions can not be clearly attributed to specific parts of the input documents. In this paper we propose ranking models that are inherently interpretable by generating explanations as a by-product of the prediction decision. We introduce the Select-and-Rank paradigm for document ranking, where we first output an explanation as a selected subset of sentences in a document. Thereafter, we solely use the explanation or selection to make the prediction, making explanations first-class citizens in the ranking process. Technically, we treat sentence selection as a latent variable trained jointly with the ranker from the final output. To that end, we propose an end-to-end training technique for Select-and-Rank models utilizing reparameterizable subset sampling using the Gumbel-max trick. We conduct extensive experiments to demonstrate that our approach is competitive to state-of-the-art methods. Our approach is broadly applicable to numerous ranking tasks and furthers the goal of building models that are interpretable by design. Finally, we present real-world applications that benefit from our sentence selection method.
A Simple and Practical Approach to Improve Misspellings in OCR Text
Authors: Junxia Lin, Johannes Ledolter
Date: 2021-06-22 00:00:00
Tasks: Optical Character Recognition (OCR)

The focus of our paper is the identification and correction of non-word errors in OCR text. Such errors may be the result of incorrect insertion, deletion, or substitution of a character, or the transposition of two adjacent characters within a single word. Or, it can be the result of word boundary problems that lead to run-on errors and incorrect-split errors. The traditional N-gram correction methods can handle single-word errors effectively. However, they show limitations when dealing with split and merge errors. In this paper, we develop an unsupervised method that can handle both errors. The method we develop leads to a sizable improvement in the correction rates. This tutorial paper addresses very difficult word correction problems - namely incorrect run-on and split errors - and illustrates what needs to be considered when addressing such problems. We outline a possible approach and assess its success on a limited study.
Zero-Shot Joint Modeling of Multiple Spoken-Text-Style Conversion Tasks using Switching Tokens
Authors: Mana Ihori, Naoki Makishima, Tomohiro Tanaka, Akihiko Takashima, Shota Orihashi, Ryo Masumura
Date: 2021-06-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Punctuation Restoration, speech-recognition, Speech Recognition, Zero-Shot Learning

In this paper, we propose a novel spoken-text-style conversion method that can simultaneously execute multiple style conversion modules such as punctuation restoration and disfluency deletion without preparing matched datasets. In practice, transcriptions generated by automatic speech recognition systems are not highly readable because they often include many disfluencies and do not include punctuation marks. To improve their readability, multiple spoken-text-style conversion modules that individually model a single conversion task are cascaded because matched datasets that simultaneously handle multiple conversion tasks are often unavailable. However, the cascading is unstable against the order of tasks because of the chain of conversion errors. Besides, the computation cost of the cascading must be higher than the single conversion. To execute multiple conversion tasks simultaneously without preparing matched datasets, our key idea is to distinguish individual conversion tasks using the on-off switch. In our proposed zero-shot joint modeling, we switch the individual tasks using multiple switching tokens, enabling us to utilize a zero-shot learning approach to executing simultaneous conversions. Our experiments on joint modeling of disfluency deletion and punctuation restoration demonstrate the effectiveness of our method.
Recognising Biomedical Names: Challenges and Solutions
Authors: Xiang Dai
Date: 2021-06-23 00:00:00
Tasks: Data Augmentation, NER

The growth rate in the amount of biomedical documents is staggering. Unlocking information trapped in these documents can enable researchers and practitioners to operate confidently in the information world. Biomedical NER, the task of recognising biomedical names, is usually employed as the first step of the NLP pipeline. Standard NER models, based on sequence tagging technique, are good at recognising short entity mentions in the generic domain. However, there are several open challenges of applying these models to recognise biomedical names: 1) Biomedical names may contain complex inner structure (discontinuity and overlapping) which cannot be recognised using standard sequence tagging technique; 2) The training of NER models usually requires large amount of labelled data, which are difficult to obtain in the biomedical domain; and, 3) Commonly used language representation models are pre-trained on generic data; a domain shift therefore exists between these models and target biomedical data. To deal with these challenges, we explore several research directions and make the following contributions: 1) we propose a transition-based NER model which can recognise discontinuous mentions; 2) We develop a cost-effective approach that nominates the suitable pre-training data; and, 3) We design several data augmentation methods for NER. Our contributions have obvious practical implications, especially when new biomedical applications are needed. Our proposed data augmentation methods can help the NER model achieve decent performance, requiring only a small amount of labelled data. Our investigation regarding selecting pre-training data can improve the model by incorporating language representation models, which are pre-trained using in-domain data. Finally, our proposed transition-based NER model can further improve the performance by recognising discontinuous mentions.
Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations
Authors: Qian Li, Hao Peng, JianXin Li, Jia Wu, Yuanxing Ning, Lihong Wang, Philip S. Yu, Zheng Wang
Date: 2021-06-23 00:00:00
Tasks: Event Extraction, Incremental Learning, reinforcement-learning, Reinforcement Learning (RL), Sentence

Event extraction is a fundamental task for natural language processing. Finding the roles of event arguments like event participants is essential for event extraction. However, doing so for real-life event descriptions is challenging because an argument's role often varies in different contexts. While the relationship and interactions between multiple arguments are useful for settling the argument roles, such information is largely ignored by existing approaches. This paper presents a better approach for event extraction by explicitly utilizing the relationships of event arguments. We achieve this through a carefully designed task-oriented dialogue system. To model the argument relation, we employ reinforcement learning and incremental learning to extract multiple arguments via a multi-turned, iterative process. Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually. It then uses the newly obtained information to improve the decisions of previously extracted arguments. This two-way feedback process allows us to exploit the argument relations to effectively settle argument roles, leading to better sentence understanding and event extraction. Experimental results show that our approach consistently outperforms seven state-of-the-art event extraction methods for the classification of events and argument role and argument identification.
End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages
Authors: Josef Jon, João Paulo Aires, Dušan Variš, Ondřej Bojar
Date: 2021-06-23 00:00:00
Proceeding: ACL 2021 5
Tasks: Machine Translation, Sentence, Translation

Lexically constrained machine translation allows the user to manipulate the output sentence by enforcing the presence or absence of certain words and phrases. Although current approaches can enforce terms to appear in the translation, they often struggle to make the constraint word form agree with the rest of the generated output. Our manual analysis shows that 46% of the errors in the output of a baseline constrained model for English to Czech translation are related to agreement. We investigate mechanisms to allow neural machine translation to infer the correct word inflection given lemmatized constraints. In particular, we focus on methods based on training the model with constraints provided as part of the input sequence. Our experiments on the English-Czech language pair show that this approach improves the translation of constrained terms in both automatic and manual evaluation by reducing errors in agreement. Our approach thus eliminates inflection errors, without introducing new errors or decreasing the overall quality of the translation.
Mixtures of Deep Neural Experts for Automated Speech Scoring
Authors: Sara Papi, Edmondo Trentin, Roberto Gretter, Marco Matassoni, Daniele Falavigna
Date: 2021-06-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Mixture-of-Experts, speech-recognition, Speech Recognition, Word Embeddings

The paper copes with the task of automatic assessment of second language proficiency from the language learners' spoken responses to test prompts. The task has significant relevance to the field of computer assisted language learning. The approach presented in the paper relies on two separate modules: (1) an automatic speech recognition system that yields text transcripts of the spoken interactions involved, and (2) a multiple classifier system based on deep learners that ranks the transcripts into proficiency classes. Different deep neural network architectures (both feed-forward and recurrent) are specialized over diverse representations of the texts in terms of: a reference grammar, the outcome of probabilistic language models, several word embeddings, and two bag-of-word models. Combination of the individual classifiers is realized either via a probabilistic pseudo-joint model, or via a neural mixture of experts. Using the data of the third Spoken CALL Shared Task challenge, the highest values to date were obtained in terms of three popular evaluation metrics.
Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language
Authors: Abdelkader El Mahdaouy, Abdellah El Mekki, Kabil Essefar, Nabil El Mamoun, Ismail Berrada, Ahmed Khoumsi
Date: 2021-06-23 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Arabic Sentiment Analysis, Multi-Task Learning, Sarcasm Detection, Sentiment Analysis

The prominence of figurative language devices, such as sarcasm and irony, poses serious challenges for Arabic Sentiment Analysis (SA). While previous research works tackle SA and sarcasm detection separately, this paper introduces an end-to-end deep Multi-Task Learning (MTL) model, allowing knowledge interaction between the two tasks. Our MTL model's architecture consists of a Bidirectional Encoder Representation from Transformers (BERT) model, a multi-task attention interaction module, and two task classifiers. The overall obtained results show that our proposed model outperforms its single-task counterparts on both SA and sarcasm detection sub-tasks.
BERT-based Multi-Task Model for Country and Province Level Modern Standard Arabic and Dialectal Arabic Identification
Authors: Abdellah El Mekki, Abdelkader El Mahdaouy, Kabil Essefar, Nabil El Mamoun, Ismail Berrada, Ahmed Khoumsi
Date: 2021-06-23 00:00:00
Tasks: Language Identification, Multi-Task Learning

Dialect and standard language identification are crucial tasks for many Arabic natural language processing applications. In this paper, we present our deep learning-based system, submitted to the second NADI shared task for country-level and province-level identification of Modern Standard Arabic (MSA) and Dialectal Arabic (DA). The system is based on an end-to-end deep Multi-Task Learning (MTL) model to tackle both country-level and province-level MSA/DA identification. The latter MTL model consists of a shared Bidirectional Encoder Representation Transformers (BERT) encoder, two task-specific attention layers, and two classifiers. Our key idea is to leverage both the task-discriminative and the inter-task shared features for country and province MSA/DA identification. The obtained results show that our MTL model outperforms single-task models on most subtasks.
Evolutionary MCMC Sampling and Optimization in Discrete Spaces
Authors: Malcolm J A Strens
Date: 2003-08-21 00:00:00
Proceeding: Proceedings of the Twentieth International Conference on International Conference on Machine Learning 2003 8
Tasks: Bayesian Inference, Diagnostic, valid

The links between genetic algorithms and
population-based Markov Chain Monte Carlo
(MCMC) methods are explored. Genetic
algorithms (GAs) are well-known for their
capability to optimize functions of discretevalued
variables, but the MCMC interpretation
allows GA variants to be used for
sampling discrete spaces (e.g. in Bayesian
inference for machine learning). The GA
crossover and mutation operators are modi
ed to provide valid MCMC samples, and a
new \exclusive-or" operator is introduced as
an alternative way to recombine population
members. This is shown to improve sampling
performance in a medical diagnostic problem
domain. The sampler can also be used within
simulated annealing to provide a global optimizer
that is similar to a GA in structure but
has known convergence properties.
Event2Mind for Russian: Understanding Emotions and Intents in Texts. Corpus and Model for Evaluation
Authors: Fenogenova A. S., Tikhonova M. I., Filipetskaya D. V., Mironenko F. D., Tabisheva A. O
Date: 2020-06-17 00:00:00
Proceeding: Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2020” 2020 6
Tasks: Common Sense Reasoning

The paper provides a comprehensive overview of the corpus for the Russian language for the commonsense inference task. Namely, we construct event phrases, which cover a wide range of everyday situations with labelled intents and reactions of the event main participant and emotions of other people involved. The dataset consists of two parts: a crowdsourced corpus of 6,756 examples from Russian sources and a translated into Russian part of the original corpus of 23,409 examples. Apart from this, we use the collected data in order to train the event2mind model for the Russian language. 
The paper presents careful description of the best Russian model and the results of the conducted experiments.
To the methodology of corpus construction for machine learning:“Taiga” syntax tree corpus and parser
Authors: Tatiana Shavrina, Olga Shapovalova
Date: 2017-07-01 00:00:00
Proceeding: “CORPORA” 2017 7

The “Taiga” project unites the corpus and the syntactic parser, being created in a new field of the corpus linguistics: the material obtained primarily meets the needs of machine learning, rather than linguistic search. The authors consider in detail the methodology for constructing the corpus, balance, volume and composition of its’ segments, format and quality of tagging—which meets the current requirements for the development of tools for processing Russian language. Within the framework of the project, the creation of a large and open-source syntactic corpus in the Universal dependencies format is planned.
Russian Paraphrasers: Paraphrase with Transformers
Authors: Alena Fenogenova
Date: 2021-04-01 00:00:00
Proceeding: BSNLP 2021 4

This paper studies the generation methods for paraphrasing in the Russian language. There are several transformer-based models (Russian and multilingual) trained on a collected corpus of paraphrases. We compare different models, contrast the quality of paraphrases using different ranking methods and apply paraphrasing methods in the context of augmentation procedure for different tasks. The contributions of the work are the combined paraphrasing dataset, fine-tuned generated models for Russian paraphrasing task and additionally the open source tool for simple usage of the paraphrasers.
Distilling the Knowledge from Conditional Normalizing Flows
Authors: Dmitry Baranchuk, Vladimir Aliev, Artem Babenko
Date: 2021-06-24 00:00:00
Proceeding: ICML Workshop INNF 2021 7
Tasks: Image Super-Resolution, Speech Synthesis, Super-Resolution

Normalizing flows are a powerful class of generative models demonstrating strong performance in several speech and vision problems. In contrast to other generative models, normalizing flows are latent variable models with tractable likelihoods and allow for stable training. However, they have to be carefully designed to represent invertible functions with efficient Jacobian determinant calculation. In practice, these requirements lead to overparameterized and sophisticated architectures that are inferior to alternative feed-forward models in terms of inference time and memory consumption. In this work, we investigate whether one can distill flow-based models into more efficient alternatives. We provide a positive answer to this question by proposing a simple distillation approach and demonstrating its effectiveness on state-of-the-art conditional flow-based models for image super-resolution and speech synthesis.