pwc-archive/papers-with-abstracts huggingface dataset:
From Bilingual to Multilingual Neural Machine Translation by Incremental Training
Authors: Carlos Escolano, Marta R. Costa-jussà, José A. R. Fonollosa
Date: 2019-06-28 00:00:00
Proceeding: ACL 2019 7
Tasks: Decoder, Machine Translation, Translation

Multilingual Neural Machine Translation approaches are based on the use of task-specific models and the addition of one more language can only be done by retraining the whole system. In this work, we propose a new training schedule that allows the system to scale to more languages without modification of the previous components based on joint training and language-independent encoder/decoder modules allowing for zero-shot translation. This work in progress shows close results to the state-of-the-art in the WMT task.
Synchronising audio and ultrasound by learning cross-modal embeddings
Authors: Aciel Eshky, Manuel Sam Ribeiro, Korin Richmond, Steve Renals
Date: 2019-07-01 00:00:00

Audiovisual synchronisation is the task of determining the time offset between speech audio and a video recording of the articulators. In child speech therapy, audio and ultrasound videos of the tongue are captured using instruments which rely on hardware to synchronise the two modalities at recording time. Hardware synchronisation can fail in practice, and no mechanism exists to synchronise the signals post hoc. To address this problem, we employ a two-stream neural network which exploits the correlation between the two modalities to find the offset. We train our model on recordings from 69 speakers, and show that it correctly synchronises 82.9% of test utterances from unseen therapy sessions and unseen speakers, thus considerably reducing the number of utterances to be manually synchronised. An analysis of model performance on the test utterances shows that directed phone articulations are more difficult to automatically synchronise compared to utterances containing natural variation in speech such as words, sentences, or conversations.
Multilingual, Multi-scale and Multi-layer Visualization of Intermediate Representations
Authors: Carlos Escolano, Marta R. Costa-jussà, Elora Lacroux, Pere-Pau Vázquez
Date: 2019-07-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Decoder, Machine Translation, Sentence, Translation, Word Embeddings

The main alternatives nowadays to deal with sequences are Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) architectures and the Transformer. In this context, RNN's, CNN's and Transformer have most commonly been used as an encoder-decoder architecture with multiple layers in each module. Far beyond this, these architectures are the basis for the contextual word embeddings which are revolutionizing most natural language downstream applications. However, intermediate layer representations in sequence-based architectures can be difficult to interpret. To make each layer representation within these architectures more accessible and meaningful, we introduce a web-based tool that visualizes them both at the sentence and token level. We present three use cases. The first analyses gender issues in contextual word embeddings. The second and third are showing multilingual intermediate representations for sentences and tokens and the evolution of these intermediate representations along the multiple layers of the decoder and in the context of multilingual machine translation.
UltraSuite: A Repository of Ultrasound and Acoustic Data from Child Speech Therapy Sessions
Authors: Aciel Eshky, Manuel Sam Ribeiro, Joanne Cleland, Korin Richmond, Zoe Roxburgh, James Scobbie, Alan Wrench
Date: 2019-07-01 00:00:00

We introduce UltraSuite, a curated repository of ultrasound and acoustic data, collected from recordings of child speech therapy sessions. This release includes three data collections, one from typically developing children and two from children with speech sound disorders. In addition, it includes a set of annotations, some manual and some automatically produced, and software tools to process, transform and visualise the data.
EGG: a toolkit for research on Emergence of lanGuage in Games
Authors: Eugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt, Marco Baroni
Date: 2019-07-01 00:00:00
Proceeding: IJCNLP 2019 11

There is renewed interest in simulating language emergence among deep neural agents that communicate to jointly solve a task, spurred by the practical aim to develop language-enabled interactive AIs, as well as by theoretical questions about the evolution of human language. However, optimizing deep architectures connected by a discrete communication channel (such as that in which language emerges) is technically challenging. We introduce EGG, a toolkit that greatly simplifies the implementation of emergent-language communication games. EGG's modular design provides a set of building blocks that the user can combine to create new games, easily navigating the optimization and architecture space. We hope that the tool will lower the technical barrier, and encourage researchers from various backgrounds to do original work in this exciting area.
HyST: A Hybrid Approach for Flexible and Accurate Dialogue State Tracking
Authors: Rahul Goel, Shachi Paul, Dilek Hakkani-Tür
Date: 2019-07-01 00:00:00
Tasks: Dialogue State Tracking, Multi-domain Dialogue State Tracking

Recent works on end-to-end trainable neural network based approaches have demonstrated state-of-the-art results on dialogue state tracking. The best performing approaches estimate a probability distribution over all possible slot values. However, these approaches do not scale for large value sets commonly present in real-life applications and are not ideal for tracking slot values that were not observed in the training set. To tackle these issues, candidate-generation-based approaches have been proposed. These approaches estimate a set of values that are possible at each turn based on the conversation history and/or language understanding outputs, and hence enable state tracking over unseen values and large value sets however, they fall short in terms of performance in comparison to the first group. In this work, we analyze the performance of these two alternative dialogue state tracking methods, and present a hybrid approach (HyST) which learns the appropriate method for each slot type. To demonstrate the effectiveness of HyST on a rich-set of slot types, we experiment with the recently released MultiWOZ-2.0 multi-domain, task-oriented dialogue-dataset. Our experiments show that HyST scales to multi-domain applications. Our best performing model results in a relative improvement of 24% and 10% over the previous SOTA and our best baseline respectively.
Post-editese: an Exacerbated Translationese
Authors: Antonio Toral
Date: 2019-07-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

Post-editing (PE) machine translation (MT) is widely used for dissemination because it leads to higher productivity than human translation from scratch (HT). In addition, PE translations are found to be of equal or better quality than HTs. However, most such studies measure quality solely as the number of errors. We conduct a set of computational analyses in which we compare PE against HT on three different datasets that cover five translation directions with measures that address different translation universals and laws of translation: simplification, normalisation and interference. We find out that PEs are simpler and more normalised and have a higher degree of interference from the source language than HTs.
Claim Extraction in Biomedical Publications using Deep Discourse Model and Transfer Learning
Authors: Titipat Achakulvisut, Chandra Bhagavatula, Daniel Acuna, Konrad Kording
Date: 2019-07-01 00:00:00
Tasks: Sentence, Transfer Learning

Claims are a fundamental unit of scientific discourse. The exponential growth in the number of scientific publications makes automatic claim extraction an important problem for researchers who are overwhelmed by this information overload. Such an automated claim extraction system is useful for both manual and programmatic exploration of scientific knowledge. In this paper, we introduce a new dataset of 1,500 scientific abstracts from the biomedical domain with expert annotations for each sentence indicating whether the sentence presents a scientific claim. We introduce a new model for claim extraction and compare it to several baseline models including rule-based and deep learning techniques. Moreover, we show that using a transfer learning approach with a fine-tuning step allows us to improve performance from a large discourse-annotated dataset. Our final model increases F1-score by over 14 percent points compared to a baseline model without transfer learning. We release a publicly accessible tool for discourse and claims prediction along with an annotation tool. We discuss further applications beyond biomedical literature.
Multiplicative Models for Recurrent Language Modeling
Authors: Diego Maupomé, Marie-Jean Meurs
Date: 2019-06-30 00:00:00
Tasks: Language Modeling, Language Modelling

Recently, there has been interest in multiplicative recurrent neural networks for language modeling. Indeed, simple Recurrent Neural Networks (RNNs) encounter difficulties recovering from past mistakes when generating sequences due to high correlation between hidden states. These challenges can be mitigated by integrating second-order terms in the hidden-state update. One such model, multiplicative Long Short-Term Memory (mLSTM) is particularly interesting in its original formulation because of the sharing of its second-order term, referred to as the intermediate state. We explore these architectural improvements by introducing new models and testing them on character-level language modeling tasks. This allows us to establish the relevance of shared parametrization in recurrent language modeling.
Hidden in Plain Sight For Too Long: Using Text Mining Techniques to Shine a Light on Workplace Sexism and Sexual Harassment
Authors: Amir Karami, Suzanne C. Swan, Cynthia Nicole White, Kayla Ford
Date: 2019-07-01 00:00:00

Objective: The goal of this study is to understand how people experience sexism and sexual harassment in the workplace by discovering themes in 2,362 experiences posted on the Everyday Sexism Project's website everydaysexism.com. Method: This study used both quantitative and qualitative methods. The quantitative method was a computational framework to collect and analyze a large number of workplace sexual harassment experiences. The qualitative method was the analysis of the topics generated by a text mining method. Results: Twenty-three topics were coded and then grouped into three overarching themes from the sex discrimination and sexual harassment literature. The Sex Discrimination theme included experiences in which women were treated unfavorably due to their sex, such as being passed over for promotion, denied opportunities, paid less than men, and ignored or talked over in meetings. The Sex Discrimination and Gender harassment theme included stories about sex discrimination and gender harassment, such as sexist hostility behaviors ranging from insults and jokes invoking misogynistic stereotypes to bullying behavior. The last theme, Unwanted Sexual Attention, contained stories describing sexual comments and behaviors used to degrade women. Unwanted touching was the highest weighted topic, indicating how common it was for website users to endure being touched, hugged or kissed, groped, and grabbed. Conclusions: This study illustrates how researchers can use automatic processes to go beyond the limits of traditional research methods and investigate naturally occurring large scale datasets on the internet to achieve a better understanding of everyday workplace sexism experiences.
Weak Supervision Enhanced Generative Network for Question Generation
Authors: Yutong Wang, Jiyuan Zheng, Qijiong Liu, Zhou Zhao, Jun Xiao, Yueting Zhuang
Date: 2019-07-01 00:00:00
Tasks: Decoder, Question Answering, Question Generation, Question-Generation

Automatic question generation according to an answer within the given passage is useful for many applications, such as question answering system, dialogue system, etc. Current neural-based methods mostly take two steps which extract several important sentences based on the candidate answer through manual rules or supervised neural networks and then use an encoder-decoder framework to generate questions about these sentences. These approaches neglect the semantic relations between the answer and the context of the whole passage which is sometimes necessary for answering the question. To address this problem, we propose the Weak Supervision Enhanced Generative Network (WeGen) which automatically discovers relevant features of the passage given the answer span in a weakly supervised manner to improve the quality of generated questions. More specifically, we devise a discriminator, Relation Guider, to capture the relations between the whole passage and the associated answer and then the Multi-Interaction mechanism is deployed to transfer the knowledge dynamically for our question generation system. Experiments show the effectiveness of our method in both automatic evaluations and human evaluations.
Ultrasound tongue imaging for diarization and alignment of child speech therapy sessions
Authors: Manuel Sam Ribeiro, Aciel Eshky, Korin Richmond, Steve Renals
Date: 2019-07-01 00:00:00
Tasks: speaker-diarization, Speaker Diarization, Word Alignment

We investigate the automatic processing of child speech therapy sessions using ultrasound visual biofeedback, with a specific focus on complementing acoustic features with ultrasound images of the tongue for the tasks of speaker diarization and time-alignment of target words. For speaker diarization, we propose an ultrasound-based time-domain signal which we call estimated tongue activity. For word-alignment, we augment an acoustic model with low-dimensional representations of ultrasound images of the tongue, learned by a convolutional neural network. We conduct our experiments using the Ultrasuite repository of ultrasound and speech recordings for child speech therapy sessions. For both tasks, we observe that systems augmented with ultrasound data outperform corresponding systems using only the audio signal.
Katecheo: A Portable and Modular System for Multi-Topic Question Answering
Authors: Shirish Hirekodi, Seban Sunny, Leonard Topno, Alwin Daniel, Daniel Whitenack, Reuben Skewes, Stuart Cranney
Date: 2019-07-01 00:00:00
Tasks: Articles, Question Answering, Reading Comprehension

We introduce a modular system that can be deployed on any Kubernetes cluster for question answering via REST API. This system, called Katecheo, includes three configurable modules that collectively enable identification of questions, classification of those questions into topics, document search, and reading comprehension. We demonstrate the system using publicly available knowledge base articles extracted from Stack Exchange sites. However, users can extend the system to any number of topics, or domains, without the need to modify any of the model serving code or train their own models. All components of the system are open source and available under a permissive Apache 2 License.
Classification of glomerular hypercellularity using convolutional features and support vector machine
Authors: Paulo Chagas, Luiz Souza, Ikaro Araújo, Nayze Aldeman, Angelo Duarte, Michele Angelo, Washington LC dos-Santos, Luciano Oliveira
Date: 2019-06-28 00:00:00
Tasks: Binary Classification, Classification, General Classification

Glomeruli are histological structures of the kidney cortex formed by interwoven blood capillaries, and are responsible for blood filtration. Glomerular lesions impair kidney filtration capability, leading to protein loss and metabolic waste retention. An example of lesion is the glomerular hypercellularity, which is characterized by an increase in the number of cell nuclei in different areas of the glomeruli. Glomerular hypercellularity is a frequent lesion present in different kidney diseases. Automatic detection of glomerular hypercellularity would accelerate the screening of scanned histological slides for the lesion, enhancing clinical diagnosis. Having this in mind, we propose a new approach for classification of hypercellularity in human kidney images. Our proposed method introduces a novel architecture of a convolutional neural network (CNN) along with a support vector machine, achieving near perfect average results with the FIOCRUZ data set in a binary classification (lesion or normal). Our deep-based classifier outperformed the state-of-the-art results on the same data set. Additionally, classification of hypercellularity sub-lesions was also performed, considering mesangial, endocapilar and both lesions; in this multi-classification task, our proposed method just failed in 4\% of the cases. To the best of our knowledge, this is the first study on deep learning over a data set of glomerular hypercellularity images of human kidney.
Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models
Authors: Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni, Wenjia Bai, Antonio de Marvao, Ozan Oktay, Christian Ledig, Loic Le Folgoc, Konstantinos Kamnitsas, Georgia Doumou, Jinming Duan, Sanjay K. Prasad, Stuart A. Cook, Declan P. O'Regan, Daniel Rueckert
Date: 2019-06-28 00:00:00
Tasks: Anatomy

Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging.
On Reducing Negative Jacobian Determinant of the Deformation Predicted by Deep Registration Networks
Authors: Dongyang Kuang
Date: 2019-06-28 00:00:00
Tasks: Image Registration, Medical Image Analysis

Image registration is a fundamental step in medical image analysis. Ideally, the transformation that registers one image to another should be a diffeomorphism that is both invertible and smooth. Traditional methods like geodesic shooting approach the problem via differential geometry, with theoretical guarantees that the resulting transformation will be smooth and invertible. Most previous research using unsupervised deep neural networks for registration have used a local smoothness constraint (typically, a spatial variation loss) to address the smoothness issue. These networks usually produce non-invertible transformations with ``folding'' in multiple voxel locations, indicated by a negative determinant of the Jacobian matrix of the transformation. While using a loss function that specifically penalizes the folding is a straightforward solution, this usually requires carefully tuning the regularization strength, especially when there are also other losses. In this paper we address this problem from a different angle, by investigating possible training mechanisms that will help the network avoid negative Jacobians and produce smoother deformations. We contribute two independent ideas in this direction. Both ideas greatly reduce the number of folding locations in the predicted deformation, without making changes to the hyperparameters or the architecture used in the existing baseline registration network.
A 1d convolutional network for leaf and time series classification
Authors: Dongyang Kuang
Date: 2019-06-28 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis, Time Series Classification

In this paper, a 1d convolutional neural network is designed for classification tasks of plant leaves. This network based classifier is analyzed in two directions. In the forward direction, the proposed network can be used in two ways: a classifier and an automatic feature extractor. As a classifier, it takes the simple centroid contour distance curve as the single feature and achieves comparable performance with state-of-art methods that usually require multiple extracted features. As a feature extractor, it produces nearly linear separable features, hence can be used together with other classifiers such as support vector machines to provide better performance. The proposed network adopts simple 1d input and is generally applicable for other tasks such as classifying one dimensional time series in an end-to-end fashion without changes. Experiments on some benchmark datasets show this architecture can provide classification accuracies that are comparable or higher than some existing methods. In the backward direction, methods like gradient-weighted class activation mapping and maximum activation map of neurons in the classification layer with respect to inputs are performed to help investigate and further validate that hidden signatures helping trigger the trained classifier's specific decisions can be human interpretable. Code for the paper is available at https://github.com/dykuang/Leaf_Project.
Robustness Guarantees for Deep Neural Networks on Videos
Authors: Min Wu, Marta Kwiatkowska
Date: 2019-06-28 00:00:00
Proceeding: CVPR 2020 6
Tasks: Optical Flow Estimation

The widespread adoption of deep learning models places demands on their robustness. In this paper, we consider the robustness of deep neural networks on videos, which comprise both the spatial features of individual frames extracted by a convolutional neural network and the temporal dynamics between adjacent frames captured by a recurrent neural network. To measure robustness, we study the maximum safe radius problem, which computes the minimum distance from the optical flow sequence obtained from a given input to that of an adversarial example in the neighbourhood of the input. We demonstrate that, under the assumption of Lipschitz continuity, the problem can be approximated using finite optimisation via discretising the optical flow space, and the approximation has provable guarantees. We then show that the finite optimisation problem can be solved by utilising a two-player turn-based game in a cooperative setting, where the first player selects the optical flows and the second player determines the dimensions to be manipulated in the chosen flow. We employ an anytime approach to solve the game, in the sense of approximating the value of the game by monotonically improving its upper and lower bounds. We exploit a gradient-based search algorithm to compute the upper bounds, and the admissible A* algorithm to update the lower bounds. Finally, we evaluate our framework on the UCF101 video dataset.