pwc-archive/papers-with-abstracts huggingface dataset:
Efficient 3D Fully Convolutional Networks for Pulmonary Lobe Segmentation in CT Images
Authors: Hoileong Lee, Tahreema Matin, Fergus Gleeson, Vicente Grau
Date: 2019-09-16 00:00:00
Tasks: Computational Efficiency, Computed Tomography (CT), Segmentation

The human lung is a complex respiratory organ, consisting of five distinct anatomic compartments called lobes. Accurate and automatic segmentation of these pulmonary lobes from computed tomography (CT) images is of clinical importance for lung disease assessment and treatment planning. However, this task is challenging due to ambiguous lobar boundaries, anatomical variations and pathological deformations. In this paper, we propose a high-resolution and efficient 3D fully convolutional network to automatically segment the lobes. We refer to the network as Pulmonary Lobe Segmentation Network (PLS-Net), which is designed to efficiently exploit 3D spatial and contextual information from high-resolution volumetric CT images for effective volume-to-volume learning and inference. The PLS-Net is based on an asymmetric encoder-decoder architecture with three novel components: (i) 3D depthwise separable convolutions to improve the network efficiency by factorising each regular 3D convolution into two simpler operations; (ii) dilated residual dense blocks to efficiently expand the receptive field of the network and aggregate multi-scale contextual information for segmentation; and (iii) input reinforcement at each downsampled resolution to compensate for the loss of spatial information due to convolutional and downsampling operations. We evaluated the proposed PLS-Net on a multi-institutional dataset that consists of 210 CT images acquired from patients with a wide range of lung abnormalities. Experimental results show that our PLS-Net achieves state-of-the-art performance with better computational efficiency. Further experiments confirm the effectiveness of each novel component of the PLS-Net.
Real-Time Variational Fisheye Stereo without Rectification and Undistortion
Authors: Menandro Roxas, Takeshi Oishi
Date: 2019-09-17 00:00:00
Tasks: Autonomous Driving, Stereo Matching, Stereo Matching Hand

Dense 3D maps from wide-angle cameras is beneficial to robotics applications such as navigation and autonomous driving. In this work, we propose a real-time dense 3D mapping method for fisheye cameras without explicit rectification and undistortion. We extend the conventional variational stereo method by constraining the correspondence search along the epipolar curve using a trajectory field induced by camera motion. We also propose a fast way of generating the trajectory field without increasing the processing time compared to conventional rectified methods. With our implementation, we were able to achieve real-time processing using modern GPUs. Our results show the advantages of our non-rectified dense mapping approach compared to rectified variational methods and non-rectified discrete stereo matching methods.
Multi-FAN: Multi-Spectral Mosaic Super-Resolution Via Multi-Scale Feature Aggregation Network
Authors: Mehrdad Shoeiby, Sadegh Aliakbarian, Saeed Anwar, Lars Petersson
Date: 2019-09-17 00:00:00
Tasks: Super-Resolution

This paper introduces a novel method to super-resolve multi-spectral images captured by modern real-time single-shot mosaic image sensors, also known as multi-spectral cameras. Our contribution is two-fold. Firstly, we super-resolve multi-spectral images from mosaic images rather than image cubes, which helps to take into account the spatial offset of each wavelength. Secondly, we introduce an external multi-scale feature aggregation network (Multi-FAN) which concatenates the feature maps with different levels of semantic information throughout a super-resolution (SR) network. A cascade of convolutional layers then implicitly selects the most valuable feature maps to generate a mosaic image. This mosaic image is then merged with the mosaic image generated by the SR network to produce a quantitatively superior image. We apply our Multi-FAN to RCAN (Residual Channel Attention Network), which is the state-of-the-art SR algorithm. We show that Multi-FAN improves both quantitative results and well as inference time.
Radiopathomics: Integration of radiographic and histologic characteristics for prognostication in glioblastoma
Authors: Saima Rathore, Muhammad A. Iftikhar, Metin N. Gurcan, Zissimos Mourelatos
Date: 2019-09-17 00:00:00
Tasks: Prognosis

Both radiographic (Rad) imaging, such as multi-parametric magnetic resonance imaging, and digital pathology (Path) images captured from tissue samples are currently acquired as standard clinical practice for glioblastoma tumors. Both these data streams have been separately used for diagnosis and treatment planning, despite the fact that they provide complementary information. In this research work, we aimed to assess the potential of both Rad and Path images in combination and comparison. An extensive set of engineered features was extracted from delineated tumor regions in Rad images, comprising T1, T1-Gd, T2, T2-FLAIR, and 100 random patches extracted from Path images. Specifically, the features comprised descriptors of intensity, histogram, and texture, mainly quantified via gray-level-co-occurrence matrix and gray-level-run-length matrices. Features extracted from images of 107 glioblastoma patients, downloaded from The Cancer Imaging Archive, were run through support vector machine for classification using leave-one-out cross-validation mechanism, and through support vector regression for prediction of continuous survival outcome. The Pearson correlation coefficient was estimated to be 0.75, 0.74, and 0.78 for Rad, Path and RadPath data. The area-under the receiver operating characteristic curve was estimated to be 0.74, 0.76 and 0.80 for Rad, Path and RadPath data, when patients were discretized into long- and short-survival groups based on average survival cutoff. Our results support the notion that synergistically using Rad and Path images may lead to better prognosis at the initial presentation of the disease, thereby facilitating the targeted enrollment of patients into clinical trials.
A Guaranteed Convergence Analysis for the Projected Fast Iterative Soft-Thresholding Algorithm in Parallel MRI
Authors: Xinlin Zhang, Hengfa Lu, Di Guo, Lijun Bao, Feng Huang, Qin Xu, Xiaobo Qu
Date: 2019-09-17 00:00:00
Tasks: compressed sensing, Open-Ended Question Answering

The boom of non-uniform sampling and compressed sensing techniques dramatically alleviates the lengthy data acquisition problem of magnetic resonance imaging. Sparse reconstruction, thanks to its fast computation and promising performance, has attracted researchers to put numerous efforts on it and has been adopted in commercial scanners. To perform sparse reconstruction, choosing a proper algorithm is essential in providing satisfying results and saving time in tuning parameters. The pFISTA, a simple and efficient algorithm for sparse reconstruction, has been successfully extended to parallel imaging. However, its convergence criterion is still an open question. And the existing convergence criterion of single-coil pFISTA cannot be applied to the parallel imaging pFISTA, which, therefore, imposes confusions and difficulties on users about determining the only parameter - step size. In this work, we provide the guaranteed convergence analysis of the parallel imaging version pFISTA to solve the two well-known parallel imaging reconstruction models, SENSE and SPIRiT. Along with the convergence analysis, we provide recommended step size values for SENSE and SPIRiT reconstructions to obtain fast and promising reconstructions. Experiments on in vivo brain images demonstrate the validity of the convergence criterion. Besides, experimental results show that compared to using backtracking and power iteration to determine the step size, our recommended step size achieves more than five times acceleration in reconstruction time in most tested cases.
Character-Centric Storytelling
Authors: Aditya Surikuchi, Jorma Laaksonen
Date: 2019-09-17 00:00:00
Tasks: Language Modeling, Language Modelling, Visual Storytelling

Sequential vision-to-language or visual storytelling has recently been one of the areas of focus in computer vision and language modeling domains. Though existing models generate narratives that read subjectively well, there could be cases when these models miss out on generating stories that account and address all prospective human and animal characters in the image sequences. Considering this scenario, we propose a model that implicitly learns relationships between provided characters and thereby generates stories with respective characters in scope. We use the VIST dataset for this purpose and report numerous statistics on the dataset. Eventually, we describe the model, explain the experiment and discuss our current status and future work.
TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data
Authors: Braden Hurl, Robin Cohen, Krzysztof Czarnecki, Steven Waslander
Date: 2019-09-17 00:00:00
Tasks: Autonomous Vehicles

Inter-vehicle communication for autonomous vehicles (AVs) stands to provide significant benefits in terms of perception robustness. We propose a novel approach for AVs to communicate perceptual observations, tempered by trust modelling of peers providing reports. Based on the accuracy of reported object detections as verified locally, communicated messages can be fused to augment perception performance beyond line of sight and at great distance from the ego vehicle. Also presented is a new synthetic dataset which can be used to test cooperative perception. The TruPercept dataset includes unreliable and malicious behaviour scenarios to experiment with some challenges cooperative perception introduces. The TruPercept runtime and evaluation framework allows modular component replacement to facilitate ablation studies as well as the creation of new trust scenarios we are able to show.
Semantic Relatedness Based Re-ranker for Text Spotting
Authors: Ahmed Sabir, Francesc Moreno-Noguer, Lluís Padró
Date: 2019-09-17 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Clustering, Dimensionality Reduction, Natural Language Inference, Semantic Similarity, Semantic Textual Similarity, Sentence, Text Spotting

Applications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based neural approaches. We present a scenario where semantic similarity is not enough, and we devise a neural approach to learn semantic relatedness. The scenario is text spotting in the wild, where a text in an image (e.g. street sign, advertisement or bus destination) must be identified and recognized. Our goal is to improve the performance of vision systems by leveraging semantic information. Our rationale is that the text to be spotted is often related to the image context in which it appears (word pairs such as Delta-airplane, or quarters-parking are not similar, but are clearly related). We show how learning a word-to-word or word-to-sentence relatedness score can improve the performance of text spotting systems up to 2.9 points, outperforming other measures in a benchmark dataset.
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle
Authors: Peter West, Ari Holtzman, Jan Buys, Yejin Choi
Date: 2019-09-16 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Abstractive Text Summarization, Extractive Summarization, Language Modeling, Language Modelling, Sentence, Sentence Summarization, Unsupervised Extractive Summarization, Unsupervised Sentence Summarization

The principle of the Information Bottleneck (Tishby et al. 1999) is to produce a summary of information X optimized to predict some other relevant information Y. In this paper, we propose a novel approach to unsupervised sentence summarization by mapping the Information Bottleneck principle to a conditional language modelling objective: given a sentence, our approach seeks a compressed sentence that can best predict the next sentence. Our iterative algorithm under the Information Bottleneck objective searches gradually shorter subsequences of the given sentence while maximizing the probability of the next sentence conditioned on the summary. Using only pretrained language models with no direct supervision, our approach can efficiently perform extractive sentence summarization over a large corpus. Building on our unsupervised extractive summarization (BottleSumEx), we then present a new approach to self-supervised abstractive summarization (BottleSumSelf), where a transformer-based language model is trained on the output summaries of our unsupervised method. Empirical results demonstrate that our extractive method outperforms other unsupervised models on multiple automatic metrics. In addition, we find that our self-supervised abstractive model outperforms unsupervised baselines (including our own) by human evaluation along multiple attributes.
Short-Text Classification Using Unsupervised Keyword Expansion
Authors: Duncan Cameron-Steinke
Date: 2019-09-16 00:00:00
Tasks: Classification, General Classification, Language Modeling, Language Modelling, Sentence, text-classification, Text Classification

Short-text classification, like all data science, struggles to achieve high performance using limited data. As a solution, a short sentence may be expanded with new and relevant feature words to form an artificially enlarged dataset, and add new features to testing data. This paper applies a novel approach to text expansion by generating new words directly for each input sentence, thus requiring no additional datasets or previous training. In this unsupervised approach, new keywords are formed within the hidden states of a pre-trained language model and then used to create extended pseudo documents. The word generation process was assessed by examining how well the predicted words matched to topics of the input sentence. It was found that this method could produce 3-10 relevant new words for each target topic, while generating just 1 word related to each non-target topic. Generated words were then added to short news headlines to create extended pseudo headlines. Experimental results have shown that models trained using the pseudo headlines can improve classification accuracy when limiting the number of training examples.
Probing Natural Language Inference Models through Semantic Fragments
Authors: Kyle Richardson, Hai Hu, Lawrence S. Moss, Ashish Sabharwal
Date: 2019-09-16 00:00:00
Tasks: Natural Language Inference

Do state-of-the-art models for language understanding already have, or can they easily learn, abilities such as boolean coordination, quantification, conditionals, comparatives, and monotonicity reasoning (i.e., reasoning about word substitutions in sentential contexts)? While such phenomena are involved in natural language inference (NLI) and go beyond basic linguistic understanding, it is unclear the extent to which they are captured in existing NLI benchmarks and effectively learned by models. To investigate this, we propose the use of semantic fragments---systematically generated datasets that each target a different semantic phenomenon---for probing, and efficiently improving, such capabilities of linguistic models. This approach to creating challenge datasets allows direct control over the semantic diversity and complexity of the targeted linguistic phenomena, and results in a more precise characterization of a model's linguistic behavior. Our experiments, using a library of 8 such semantic fragments, reveal two remarkable findings: (a) State-of-the-art models, including BERT, that are pre-trained on existing NLI benchmark datasets perform poorly on these new fragments, even though the phenomena probed here are central to the NLI task. (b) On the other hand, with only a few minutes of additional fine-tuning---with a carefully selected learning rate and a novel variation of "inoculation"---a BERT-based model can master all of these logic and monotonicity fragments while retaining its performance on established NLI benchmarks.
Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation
Authors: Chengyi Wang, Yu Wu, Shujie Liu, Zhenglu Yang, Ming Zhou
Date: 2019-09-17 00:00:00
Tasks: Multi-Task Learning, Translation

End-to-end speech translation, a hot topic in recent years, aims to translate a segment of audio into a specific language with an end-to-end model. Conventional approaches employ multi-task learning and pre-training methods for this task, but they suffer from the huge gap between pre-training and fine-tuning. To address these issues, we propose a Tandem Connectionist Encoding Network (TCEN) which bridges the gap by reusing all subnets in fine-tuning, keeping the roles of subnets consistent, and pre-training the attention module. Furthermore, we propose two simple but effective methods to guarantee the speech encoder outputs and the MT encoder inputs are consistent in terms of semantic representation and sequence length. Experimental results show that our model outperforms baselines 2.2 BLEU on a large benchmark dataset.
Grounding learning of modifier dynamics: An application to color naming
Authors: Xudong Han, Philip Schulz, Trevor Cohn
Date: 2019-09-17 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Additive models, Natural Language Understanding

Grounding is crucial for natural language understanding. An important subtask is to understand modified color expressions, such as 'dirty blue'. We present a model of color modifiers that, compared with previous additive models in RGB space, learns more complex transformations. In addition, we present a model that operates in the HSV color space. We show that certain adjectives are better modeled in that space. To account for all modifiers, we train a hard ensemble model that selects a color space depending on the modifier color pair. Experimental results show significant and consistent improvements compared to the state-of-the-art baseline model.
Learning Explicit and Implicit Structures for Targeted Sentiment Analysis
Authors: Hao Li, Wei Lu
Date: 2019-09-17 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Sentiment Analysis

Targeted sentiment analysis is the task of jointly predicting target entities and their associated sentiment information. Existing research efforts mostly regard this joint task as a sequence labeling problem, building models that can capture explicit structures in the output space. However, the importance of capturing implicit global structural information that resides in the input space is largely unexplored. In this work, we argue that both types of information (implicit and explicit structural information) are crucial for building a successful targeted sentiment analysis model. Our experimental results show that properly capturing both information is able to lead to better performance than competitive existing approaches. We also conduct extensive experiments to investigate our model's effectiveness and robustness.
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering
Authors: Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Hong Wang, Shiyu Chang, Murray Campbell, William Yang Wang
Date: 2019-09-17 00:00:00
Proceeding: WS 2019 11
Tasks: Information Retrieval, Multi-hop Question Answering, Question Answering, Reading Comprehension, Retrieval

A key challenge of multi-hop question answering (QA) in the open-domain setting is to accurately retrieve the supporting passages from a large corpus. Existing work on open-domain QA typically relies on off-the-shelf information retrieval (IR) techniques to retrieve \textbf{answer passages}, i.e., the passages containing the groundtruth answers. However, IR-based approaches are insufficient for multi-hop questions, as the topic of the second or further hops is not explicitly covered by the question. To resolve this issue, we introduce a new sub-problem of open-domain multi-hop QA, which aims to recognize the bridge (\emph{i.e.}, the anchor that links to the answer passage) from the context of a set of start passages with a reading comprehension model. This model, the \textbf{bridge reasoner}, is trained with a weakly supervised signal and produces the candidate answer passages for the \textbf{passage reader} to extract the answer. On the full-wiki HotpotQA benchmark, we significantly improve the baseline method by 14 point F1. Without using any memory-inefficient contextual embeddings, our result is also competitive with the state-of-the-art that applies BERT in multiple modules.
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering
Authors: Ameya Godbole, Dilip Kavarthapu, Rajarshi Das, Zhiyu Gong, Abhishek Singhal, Hamed Zamani, Mo Yu, Tian Gao, Xiaoxiao Guo, Manzil Zaheer, Andrew McCallum
Date: 2019-09-17 00:00:00
Proceeding: WS 2019 11
Tasks: Information Retrieval, Multi-hop Question Answering, Question Answering, Retrieval

Multi-hop question answering (QA) requires an information retrieval (IR) system that can find \emph{multiple} supporting evidence needed to answer the question, making the retrieval process very challenging. This paper introduces an IR technique that uses information of entities present in the initially retrieved evidence to learn to `\emph{hop}' to other relevant evidence. In a setting, with more than \textbf{5 million} Wikipedia paragraphs, our approach leads to significant boost in retrieval performance. The retrieved evidence also increased the performance of an existing QA model (without any training) on the \hotpot benchmark by \textbf{10.59} F1.
SocialNLP EmotionX 2019 Challenge Overview: Predicting Emotions in Spoken Dialogues and Chats
Authors: Boaz Shmueli, Lun-Wei Ku
Date: 2019-09-17 00:00:00

We present an overview of the EmotionX 2019 Challenge, held at the 7th International Workshop on Natural Language Processing for Social Media (SocialNLP), in conjunction with IJCAI 2019. The challenge entailed predicting emotions in spoken and chat-based dialogues using augmented EmotionLines datasets. EmotionLines contains two distinct datasets: the first includes excerpts from a US-based TV sitcom episode scripts (Friends) and the second contains online chats (EmotionPush). A total of thirty-six teams registered to participate in the challenge. Eleven of the teams successfully submitted their predictions performance evaluation. The top-scoring team achieved a micro-F1 score of 81.5% for the spoken-based dialogues (Friends) and 79.5% for the chat-based dialogues (EmotionPush).
Pointer-based Fusion of Bilingual Lexicons into Neural Machine Translation
Authors: Jetic Gū, Hassan S. Shavarani, Anoop Sarkar
Date: 2019-09-17 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Machine Translation, NMT, Translation

Neural machine translation (NMT) systems require large amounts of high quality in-domain parallel corpora for training. State-of-the-art NMT systems still face challenges related to out-of-vocabulary words and dealing with low-resource language pairs. In this paper, we propose and compare several models for fusion of bilingual lexicons with an end-to-end trained sequence-to-sequence model for machine translation. The result is a fusion model with two information sources for the decoder: a neural conditional language model and a bilingual lexicon. This fusion model learns how to combine both sources of information in order to produce higher quality translation output. Our experiments show that our proposed models work well in relatively low-resource scenarios, and also effectively reduce the parameter size and training cost for NMT without sacrificing performance.