pwc-archive/papers-with-abstracts huggingface dataset:
Video Compression With Rate-Distortion Autoencoders
Authors: Amirhossein Habibian, Ties van Rozendaal, Jakub M. Tomczak, Taco S. Cohen
Date: 2019-08-14 00:00:00
Proceeding: ICCV 2019 10
Tasks: Motion Compensation, Semantic Compression, Video Compression

In this paper we present a a deep generative model for lossy video compression. We employ a model that consists of a 3D autoencoder with a discrete latent space and an autoregressive prior used for entropy coding. Both autoencoder and prior are trained jointly to minimize a rate-distortion loss, which is closely related to the ELBO used in variational autoencoders. Despite its simplicity, we find that our method outperforms the state-of-the-art learned video compression networks based on motion compensation or interpolation. We systematically evaluate various design choices, such as the use of frame-based or spatio-temporal autoencoders, and the type of autoregressive prior. In addition, we present three extensions of the basic method that demonstrate the benefits over classical approaches to compression. First, we introduce semantic compression, where the model is trained to allocate more bits to objects of interest. Second, we study adaptive compression, where the model is adapted to a domain with limited variability, e.g., videos taken from an autonomous car, to achieve superior compression on that domain. Finally, we introduce multimodal compression, where we demonstrate the effectiveness of our model in joint compression of multiple modalities captured by non-standard imaging sensors, such as quad cameras. We believe that this opens up novel video compression applications, which have not been feasible with classical codecs.
On-Device Text Representations Robust To Misspellings via Projections
Authors: Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva
Date: 2019-08-14 00:00:00
Proceeding: EACL 2021 2
Tasks: Text Classification, Word Embeddings

Recently, there has been a strong interest in developing natural language applications that live on personal devices such as mobile phones, watches and IoT with the objective to preserve user privacy and have low memory. Advances in Locality-Sensitive Hashing (LSH)-based projection networks have demonstrated state-of-the-art performance in various classification tasks without explicit word (or word-piece) embedding lookup tables by computing on-the-fly text representations. In this paper, we show that the projection based neural classifiers are inherently robust to misspellings and perturbations of the input text. We empirically demonstrate that the LSH projection based classifiers are more robust to common misspellings compared to BiLSTMs (with both word-piece & word-only tokenization) and fine-tuned BERT based methods. When subject to misspelling attacks, LSH projection based classifiers had a small average accuracy drop of 2.94% across multiple classifications tasks, while the fine-tuned BERT model accuracy had a significant drop of 11.44%.
Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation
Authors: Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini, Rasha Obeidat, Prasad Tadepalli
Date: 2019-08-14 00:00:00
Tasks: Entity Disambiguation, Language Modeling, Language Modelling

We present a new local entity disambiguation system. The key to our system is a novel approach for learning entity representations. In our approach we learn an entity aware extension of Embedding for Language Model (ELMo) which we call Entity-ELMo (E-ELMo). Given a paragraph containing one or more named entity mentions, each mention is first defined as a function of the entire paragraph (including other mentions), then they predict the referent entities. Utilizing E-ELMo for local entity disambiguation, we outperform all of the state-of-the-art local and global models on the popular benchmarks by improving about 0.5\% on micro average accuracy for AIDA test-b with Yago candidate set. The evaluation setup of the training data and candidate set are the same as our baselines for fair comparison.
Skin Lesion Segmentation and Classification for ISIC 2018 by Combining Deep CNN and Handcrafted Features
Authors: Redha Ali, Russell C. Hardie, Manawaduge Supun De Silva, Temesguen Messay Kebede
Date: 2019-08-14 00:00:00
Tasks: Decision Making, General Classification, Lesion Segmentation, Skin Lesion Segmentation

This short report describes our submission to the ISIC 2018 Challenge in Skin Lesion Analysis Towards Melanoma Detection for Task1 and Task 3. This work has been accomplished by a team of researchers at the University of Dayton Signal and Image Processing Lab. Our proposed approach is computationally efficient are combines information from both deep learning and handcrafted features. For Task3, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single method features. These features are utilized as inputs to a decision-making model that is based on a multiclass Support Vector Machine (SVM) classifier. The proposed technique is evaluated on online validation databases. Our score was 0.841 with SVM classifier on the validation dataset.
Applications of Linear Defeasible Logic: combining resource consumption and exceptions to energy management and business processes
Authors: Francesco Olivieri, Guido Governatori, Claudio Tomazzoli, Matteo Cristani
Date: 2019-08-14 00:00:00
Tasks: energy management, Management

Linear Logic and Defeasible Logic have been adopted to formalise different features of knowledge representation: consumption of resources, and non monotonic reasoning in particular to represent exceptions. Recently, a framework to combine sub-structural features, corresponding to the consumption of resources, with defeasibility aspects to handle potentially conflicting information, has been discussed in literature, by some of the authors. Two applications emerged that are very relevant: energy management and business process management. We illustrate a set of guide lines to determine how to apply linear defeasible logic to those contexts.
Trustable and Automated Machine Learning Running with Blockchain and Its Applications
Authors: Tao Wang, Xinmin Wu, Taiping He
Date: 2019-08-14 00:00:00
Tasks: BIG-bench Machine Learning, Fraud Detection, Synthetic Data Generation

Machine learning algorithms learn from data and use data from databases that are mutable; therefore, the data and the results of machine learning cannot be fully trusted. Also, the machine learning process is often difficult to automate. A unified analytical framework for trustable machine learning has been presented in the literature. It proposed building a trustable machine learning system by using blockchain technology, which can store data in a permanent and immutable way. In addition, smart contracts on blockchain are used to automate the machine learning process. In the proposed framework, a core machine learning algorithm can have three implementations: server layer implementation, streaming layer implementation, and smart contract implementation. However, there are still open questions. First, the streaming layer usually deploys on edge devices and therefore has limited memory and computing power. How can we run machine learning on the streaming layer? Second, most data that are stored on blockchain are financial transactions, for which fraud detection is often needed. However, in some applications, training data are hard to obtain. Can we build good machine learning models to do fraud detection with limited training data? These questions motivated this paper; which makes two contributions. First, it proposes training a machine learning model on the server layer and saving the model with a special binary data format. Then, the streaming layer can take this blob of binary data as input and score incoming data online. The blob of binary data is very compact and can be deployed on edge devices. Second, the paper presents a new method of synthetic data generation that can enrich the training data set. Experiments show that this synthetic data generation is very effective in applications such as fraud detection in financial data.
BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling Tasks
Authors: Shreyas Sharma, Ron Daniel Jr
Date: 2019-08-13 00:00:00
Tasks: Medical Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Biomedical Named Entity Recognition (NER) is a challenging problem in biomedical information processing due to the widespread ambiguity of out of context terms and extensive lexical variations. Performance on bioNER benchmarks continues to improve due to advances like BERT, GPT, and XLNet. FLAIR (1) is an alternative embedding model which is less computationally intensive than the others mentioned. We test FLAIR and its pretrained PubMed embeddings (which we term BioFLAIR) on a variety of bio NER tasks and compare those with results from BERT-type networks. We also investigate the effects of a small amount of additional pretraining on PubMed content, and of combining FLAIR and ELMO models. We find that with the provided embeddings, FLAIR performs on-par with the BERT networks - even establishing a new state of the art on one benchmark. Additional pretraining did not provide a clear benefit, although this might change with even more pretraining being done. Stacking the FLAIR embeddings with others typically does provide a boost in the benchmark results.
Variational Fusion for Multimodal Sentiment Analysis
Authors: Navonil Majumder, Soujanya Poria, Gangeshwar Krishnamurthy, Niyati Chhaya, Rada Mihalcea, Alexander Gelbukh
Date: 2019-08-13 00:00:00
Tasks: Multimodal Sentiment Analysis, Question Answering, Sentiment Analysis

Multimodal fusion is considered a key step in multimodal tasks such as sentiment analysis, emotion detection, question answering, and others. Most of the recent work on multimodal fusion does not guarantee the fidelity of the multimodal representation with respect to the unimodal representations. In this paper, we propose a variational autoencoder-based approach for modality fusion that minimizes information loss between unimodal and multimodal representations. We empirically show that this method outperforms the state-of-the-art methods by a significant margin on several popular datasets.
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods
Authors: Pushkar Mishra, Helen Yannakoudakis, Ekaterina Shutova
Date: 2019-08-13 00:00:00
Tasks: Abuse Detection, Ethics, Survey

Abuse on the Internet represents an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse on online platforms. The psychological effects of such abuse on individuals can be profound and lasting. Consequently, over the past few years, there has been a substantial research effort towards automated abuse detection in the field of natural language processing (NLP). In this paper, we present a comprehensive survey of the methods that have been proposed to date, thus providing a platform for further development of this area. We describe the existing datasets and review the computational approaches to abuse detection, analyzing their strengths and limitations. We discuss the main trends that emerge, highlight the challenges that remain, outline possible solutions, and propose guidelines for ethics and explainability
Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features
Authors: Rahim Taheri, Meysam Ghahramani, Reza Javidan, Mohammad Shojafar, Zahra Pooranian, Mauro Conti
Date: 2019-08-13 00:00:00
Tasks: Android Malware Detection, Malware Detection

In this paper, we develop four malware detection methods using Hamming distance to find similarity between samples which are first nearest neighbors (FNN), all nearest neighbors (ANN), weighted all nearest neighbors (WANN), and k-medoid based nearest neighbors (KMNN). In our proposed methods, we can trigger the alarm if we detect an Android app is malicious. Hence, our solutions help us to avoid the spread of detected malware on a broader scale. We provide a detailed description of the proposed detection methods and related algorithms. We include an extensive analysis to asses the suitability of our proposed similarity-based detection methods. In this way, we perform our experiments on three datasets, including benign and malware Android apps like Drebin, Contagio, and Genome. Thus, to corroborate the actual effectiveness of our classifier, we carry out performance comparisons with some state-of-the-art classification and malware detection algorithms, namely Mixed and Separated solutions, the program dissimilarity measure based on entropy (PDME) and the FalDroid algorithms. We test our experiments in a different type of features: API, intent, and permission features on these three datasets. The results confirm that accuracy rates of proposed algorithms are more than 90% and in some cases (i.e., considering API features) are more than 99%, and are comparable with existing state-of-the-art solutions.
Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources
Authors: Daniel Specht Menezes, Pedro Savarese, Ruy Luiz Milidiú
Date: 2019-08-13 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

With the recent progress in machine learning, boosted by techniques such as deep learning, many tasks can be successfully solved once a large enough dataset is available for training. Nonetheless, human-annotated datasets are often expensive to produce, especially when labels are fine-grained, as is the case of Named Entity Recognition (NER), a task that operates with labels on a word-level. In this paper, we propose a method to automatically generate labeled datasets for NER from public data sources by exploiting links and structured data from DBpedia and Wikipedia. Due to the massive size of these data sources, the resulting dataset -- SESAME Available at https://sesame-pt.github.io -- is composed of millions of labeled sentences. We detail the method to generate the dataset, report relevant statistics, and design a baseline using a neural network, showing that our dataset helps building better NER predictors.
Incorporating Source Syntax into Transformer-Based Neural Machine Translation
Authors: Anna Currey, Kenneth Heafield
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Decoder, Machine Translation, NMT, Translation

Transformer-based neural machine translation (NMT) has recently achieved state-of-the-art performance on many machine translation tasks. However, recent work (Raganato and Tiedemann, 2018; Tang et al., 2018; Tran et al., 2018) has indicated that Transformer models may not learn syntactic structures as well as their recurrent neural network-based counterparts, particularly in low-resource cases. In this paper, we incorporate constituency parse information into a Transformer NMT model. We leverage linearized parses of the source training sentences in order to inject syntax into the Transformer architecture without modifying it. We introduce two methods: a multi-task machine translation and parsing model with a single encoder and decoder, and a mixed encoder model that learns to translate directly from parsed and unparsed source sentences. We evaluate our methods on low-resource translation from English into twenty target languages, showing consistent improvements of 1.3 BLEU on average across diverse target languages for the multi-task technique. We further evaluate the models on full-scale WMT tasks, finding that the multi-task model aids low- and medium-resource NMT but degenerates high-resource English-German translation.
Customizing Neural Machine Translation for Subtitling
Authors: Evgeny Matusov, Patrick Wilken, Yota Georgakopoulou
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Segmentation, Sentence, Translation

In this work, we customized a neural machine translation system for translation of subtitles in the domain of entertainment. The neural translation model was adapted to the subtitling content and style and extended by a simple, yet effective technique for utilizing inter-sentence context for short sentences such as dialog turns. The main contribution of the paper is a novel subtitle segmentation algorithm that predicts the end of a subtitle line given the previous word-level context using a recurrent neural network learned from human segmentation decisions. This model is combined with subtitle length and duration constraints established in the subtitling industry. We conducted a thorough human evaluation with two post-editors (English-to-Spanish translation of a documentary and a sitcom). It showed a notable productivity increase of up to 37{\%} as compared to translating from scratch and significant reductions in human translation edit rate in comparison with the post-editing of the baseline non-adapted system without a learned segmentation model.
Integration of Dubbing Constraints into Machine Translation
Authors: Ashutosh Saboo, Timo Baumann
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Decoder, Machine Translation, Translation

Translation systems aim to perform a meaning-preserving conversion of linguistic material (typically text but also speech) from a source to a target language (and, to a lesser degree, the corresponding socio-cultural contexts). Dubbing, i.e., the lip-synchronous translation and revoicing of speech adds to this constraints about the close matching of phonetic and resulting visemic synchrony characteristics of source and target material. There is an inherent conflict between a translation{'}s meaning preservation and {`}dubbability{'} and the resulting trade-off can be controlled by weighing the synchrony constraints. We introduce our work, which to the best of our knowledge is the first of its kind, on integrating synchrony constraints into the machine translation paradigm. We present first results for the integration of synchrony constraints into encoder decoder-based neural machine translation and show that considerably more {`}dubbable{'} translations can be achieved with only a small impact on BLEU score, and dubbability improves more steeply than BLEU degrades.
Findings of the 2019 Conference on Machine Translation (WMT19)
Authors: Lo{\"\i}c Barrault, Ond{\v{r}}ej Bojar, Marta R. Costa-juss{\`a}, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, Shervin Malmasi, Christof Monz, Mathias M{\"u}ller, Santanu Pal, Matt Post, Marcos Zampieri
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation.
Results of the WMT19 Metrics Shared Task: Segment-Level and Strong MT Systems Pose Big Challenges
Authors: Qingsong Ma, Johnny Wei, Ond{\v{r}}ej Bojar, Yvette Graham
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Translation

This paper presents the results of the WMT19 Metrics Shared Task. Participants were asked to score the outputs of the translations systems competing in the WMT19 News Translation Task with automatic metrics. 13 research groups submitted 24 metrics, 10 of which are reference-less {``}metrics{''} and constitute submissions to the joint task with WMT19 Quality Estimation Task, {``}QE as a Metric{''}. In addition, we computed 11 baseline metrics, with 8 commonly applied baselines (BLEU, SentBLEU, NIST, WER, PER, TER, CDER, and chrF) and 3 reimplementations (chrF+, sacreBLEU-BLEU, and sacreBLEU-chrF). Metrics were evaluated on the system level, how well a given metric correlates with the WMT19 official manual ranking, and segment level, how well the metric correlates with human judgements of segment quality. This year, we use direct assessment (DA) as our only form of manual evaluation.
GTCOM Neural Machine Translation Systems for WMT19
Authors: Chao Bei, Hao Zong, Conghu Yuan, Qingming Liu, Baoyong Fan
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Knowledge Distillation, Language Modeling, Language Modelling, Machine Translation, Reranking, Translation

This paper describes the Global Tone Communication Co., Ltd.{'}s submission of the WMT19 shared news translation task. We participate in six directions: English to (Gujarati, Lithuanian and Finnish) and (Gujarati, Lithuanian and Finnish) to English. Further, we get the best BLEU scores in the directions of English to Gujarati and Lithuanian to English (28.2 and 36.3 respectively) among all the participants. The submitted systems mainly focus on back-translation, knowledge distillation and reranking to build a competitive model for this task. Also, we apply language model to filter monolingual data, back-translated data and parallel data. The techniques we apply for data filtering include filtering by rules, language models. Besides, We conduct several experiments to validate different knowledge distillation techniques and right-to-left (R2L) reranking.
Machine Translation with parfda, Moses, kenlm, nplm, and PRO
Authors: Ergun Bi{\c{c}}ici
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

We build parfda Moses statistical machine translation (SMT) models for most language pairs in the news translation task. We experiment with a hybrid approach using neural language models integrated into Moses. We obtain the constrained data statistics on the machine translation task, the coverage of the test sets, and the upper bounds on the translation results. We also contribute a new testsuite for the German-English language pair and a new automated key phrase extraction technique for the evaluation of the testsuite translations.
LIUM's Contributions to the WMT2019 News Translation Task: Data and Systems for German-French Language Pairs
Authors: Fethi Bougares, Jane Wottawa, Anne Baillot, Lo{\"\i}c Barrault, Adrien Bardet
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, NMT, Translation

This paper describes the neural machine translation (NMT) systems of the LIUM Laboratory developed for the French↔German news translation task of the Fourth Conference onMachine Translation (WMT 2019). The chosen language pair is included for the first time in the WMT news translation task. We de-scribe how the training and the evaluation data was created. We also present our participation in the French↔German translation directions using self-attentional Transformer networks with small and big architectures.
The University of Maryland's Kazakh-English Neural Machine Translation System at WMT19
Authors: Eleftheria Briakou, Marine Carpuat
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Transfer Learning, Translation

This paper describes the University of Maryland{'}s submission to the WMT 2019 Kazakh-English news translation task. We study the impact of transfer learning from another low-resource but related language. We experiment with different ways of encoding lexical units to maximize lexical overlap between the two language pairs, as well as back-translation and ensembling. The submitted system improves over a Kazakh-only baseline by +5.45 BLEU on newstest2019.