pwc-archive/papers-with-abstracts huggingface dataset:
Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting
Authors: Jaejun Lee, Raphael Tang, Jimmy Lin
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Keyword Spotting

Used for simple commands recognition on devices from smart speakers to mobile phones, keyword spotting systems are everywhere. Ubiquitous as well are web applications, which have grown in popularity and complexity over the last decade. However, despite their obvious advantages in natural language interaction, voice-enabled web applications are still few and far between. We attempt to bridge this gap with Honkling, a novel, JavaScript-based keyword spotting system. Purely client-side and cross-device compatible, Honkling can be deployed directly on user devices. Our in-browser implementation enables seamless personalization, which can greatly improve model quality; in the presence of underrepresented, non-American user accents, we can achieve up to an absolute 10{\%} increase in accuracy in the personalized model with only a few examples.
IFlyLegal: A Chinese Legal System for Consultation, Law Searching, and Document Analysis
Authors: Ziyue Wang, Baoxin Wang, Xingyi Duan, Dayong Wu, Shijin Wang, Guoping Hu, Ting Liu
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Natural Language Inference, Question Answering, Text Categorization

Legal Tech is developed to help people with legal services and solve legal problems via machines. To achieve this, one of the key requirements for machines is to utilize legal knowledge and comprehend legal context. This can be fulfilled by natural language processing (NLP) techniques, for instance, text representation, text categorization, question answering (QA) and natural language inference, etc. To this end, we introduce a freely available Chinese Legal Tech system (IFlyLegal) that benefits from multiple NLP tasks. It is an integrated system that performs legal consulting, multi-way law searching, and legal document analysis by exploiting techniques such as deep contextual representations and various attention mechanisms. To our knowledge, IFlyLegal is the first Chinese legal system that employs up-to-date NLP techniques and caters for needs of different user groups, such as lawyers, judges, procurators, and clients. Since Jan, 2019, we have gathered 2,349 users and 28,238 page views (till June, 23, 2019).
INMT: Interactive Neural Machine Translation Prediction
Authors: Sebastin Santy, D, S apat, ipan, Monojit Choudhury, Kalika Bali
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Machine Translation, Prediction, Translation

In this paper, we demonstrate an Interactive Machine Translation interface, that assists human translators with on-the-fly hints and suggestions. This makes the end-to-end translation process faster, more efficient and creates high-quality translations. We augment the OpenNMT backend with a mechanism to accept the user input and generate conditioned translations.
Journalist-in-the-Loop: Continuous Learning as a Service for Rumour Analysis
Authors: Twin Karmakharm, Nikolaos Aletras, Kalina Bontcheva
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Rumour Detection

Automatically identifying rumours in social media and assessing their veracity is an important task with downstream applications in journalism. A significant challenge is how to keep rumour analysis tools up-to-date as new information becomes available for particular rumours that spread in a social network. This paper presents a novel open-source web-based rumour analysis tool that can continuous learn from journalists. The system features a rumour annotation service that allows journalists to easily provide feedback for a given social media post through a web-based interface. The feedback allows the system to improve an underlying state-of-the-art neural network-based rumour classification model. The system can be easily integrated as a service into existing tools and platforms used by journalists using a REST API.
MAssistant: A Personal Knowledge Assistant for MOOC Learners
Authors: Lan Jiang, Shuhan Hu, Mingyu Huang, Zhichun Wang, Jinjian Yang, Xiaoju Ye, Wei Zheng
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Management

Massive Open Online Courses (MOOCs) have developed rapidly and attracted large number of learners. In this work, we present MAssistant system, a personal knowledge assistant for MOOC learners. MAssistant helps users to trace the concepts they have learned in MOOCs, and to build their own concept graphs. There are three key components in MAssistant: (i) a large-scale concept graph built from open data sources, which contains concepts in various domains and relations among them; (ii) a browser extension which interacts with learners when they are watching video lectures, and presents important concepts to them; (iii) a web application allowing users to explore their personal concept graphs, which are built based on their learning activities on MOOCs. MAssistant will facilitate the knowledge management task for MOOC learners, and make the learning on MOOCs easier.
Memory Grounded Conversational Reasoning
Authors: Seungwhan Moon, Pararth Shah, Rajen Subba, Anuj Kumar
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11

We demonstrate a conversational system which engages the user through a multi-modal, multi-turn dialog over the user{'}s memories. The system can perform QA over memories by responding to user queries to recall specific attributes and associated media (e.g. photos) of past episodic memories. The system can also make proactive suggestions to surface related events or facts from past memories to make conversations more engaging and natural. To implement such a system, we collect a new corpus of memory grounded conversations, which comprises human-to-human role-playing dialogs given synthetic memory graphs with simulated attributes. Our proof-of-concept system operates on these synthetic memory graphs, however it can be trained and applied to real-world user memory data (e.g. photo albums, etc.) We present the architecture of the proposed conversational system, and example queries that the system supports.
MY-AKKHARA: A Romanization-based Burmese (Myanmar) Input Method
Authors: Chenchen Ding, Masao Utiyama, Eiichiro Sumita
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11

MY-AKKHARA is a method used to input Burmese texts encoded in the Unicode standard, based on commonly accepted Latin transcription. By using this method, arbitrary Burmese strings can be accurately inputted with 26 lowercase Latin letters. Meanwhile, the 26 uppercase Latin letters are designed as shortcuts of lowercase letter sequences. The frequency of Burmese characters is considered in MY-AKKHARA to realize an efficient keystroke distribution on a QWERTY keyboard. Given that the Unicode standard has not been extensively used in digitization of Burmese, we hope that MY-AKKHARA can contribute to the widespread use of Unicode in Myanmar and can provide a platform for smart input methods for Burmese in the future. An implementation of MY-AKKHARA running in Windows is released at http://www2.nict.go.jp/astrec-att/member/ding/my-akkhara.html
PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules
Authors: Youngsoo Jang, Jongmin Lee, Jaeyoung Park, Kyeng-Hun Lee, Pierre Lison, Kee-Eung Kim
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Deep Learning, Dialogue Management, Dialogue State Tracking, Management, speech-recognition, Speech Recognition, Spoken Dialogue Systems, Text Generation

We present PyOpenDial, a Python-based domain-independent, open-source toolkit for spoken dialogue systems. Recent advances in core components of dialogue systems, such as speech recognition, language understanding, dialogue management, and language generation, harness deep learning to achieve state-of-the-art performance. The original OpenDial, implemented in Java, provides a plugin architecture to integrate external modules, but lacks Python bindings, making it difficult to interface with popular deep learning frameworks such as Tensorflow or PyTorch. To this end, we re-implemented OpenDial in Python and extended the toolkit with a number of novel functionalities for neural dialogue state tracking and action planning. We describe the overall architecture and its extensions, and illustrate their use on an example where the system response model is implemented with a recurrent neural network.
Redcoat: A Collaborative Annotation Tool for Hierarchical Entity Typing
Authors: Michael Stewart, Wei Liu, Rachel Cardell-Oliver
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Entity Typing

We introduce Redcoat, a web-based annotation tool that supports collaborative hierarchical entity typing. As an annotation tool, Redcoat also facilitates knowledge elicitation by allowing the creation and continuous refinement of concept hierarchies during annotation. It aims to minimise not only annotation time but the time it takes for project creators to set up and distribute projects to annotators. Projects created using the web-based interface can be rapidly distributed to a list of email addresses. Redcoat handles the propagation of documents amongst annotators and automatically scales the annotation workload depending on the number of active annotators. In this paper we discuss these key features and outline Redcoat{'}s system architecture. We also highlight Redcoat{'}s unique benefits over existing annotation tools via a qualitative comparison.
SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval
Authors: Fabian David Schmidt, Markus Dietsche, Simone Paolo Ponzetto, Goran Glava{\v{s}}
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Information Retrieval, Retrieval, Sentence, Sentence Retrieval, Word Embeddings

We introduce Seagle, a platform for comparative evaluation of semantic text encoding models on information retrieval (IR) tasks. Seagle implements (1) word embedding aggregators, which represent texts as algebraic aggregations of pretrained word embeddings and (2) pretrained semantic encoders, and allows for their comparative evaluation on arbitrary (monolingual and cross-lingual) IR collections. We benchmark Seagle{'}s models on monolingual document retrieval and cross-lingual sentence retrieval. Seagle functionality can be exploited via an easy-to-use web interface and its modular backend (micro-service architecture) can easily be extended with additional semantic search models.
A Stylometry Toolkit for Latin Literature
Authors: Thomas J. Bolt, Jeffrey H. Flynt, Pramit Chaudhuri, Joseph P. Dexter
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11

Computational stylometry has become an increasingly important aspect of literary criticism, but many humanists lack the technical expertise or language-specific NLP resources required to exploit computational methods. We demonstrate a stylometry toolkit for analysis of Latin literary texts, which is freely available at www.qcrit.org/stylometry. Our toolkit generates data for a diverse range of literary features and has an intuitive point-and-click interface. The features included have proven effective for multiple literary studies and are calculated using custom heuristics without the need for syntactic parsing. As such, the toolkit models one approach to the user-friendly generation of stylometric data, which could be extended to other premodern and non-English languages underserved by standard NLP resources.
A System for Diacritizing Four Varieties of Arabic
Authors: Hamdy Mubarak, Ahmed Abdelali, Kareem Darwish, Mohamed Eldesouki, Younes Samih, Hassan Sajjad
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Feature Engineering, text-to-speech, Text to Speech

Short vowels, aka diacritics, are more often omitted when writing different varieties of Arabic including Modern Standard Arabic (MSA), Classical Arabic (CA), and Dialectal Arabic (DA). However, diacritics are required to properly pronounce words, which makes diacritic restoration (a.k.a. diacritization) essential for language learning and text-to-speech applications. In this paper, we present a system for diacritizing MSA, CA, and two varieties of DA, namely Moroccan and Tunisian. The system uses a character level sequence-to-sequence deep learning model that requires no feature engineering and beats all previous SOTA systems for all the Arabic varieties that we test on.
TellMeWhy: Learning to Explain Corrective Feedback for Second Language Learners
Authors: Yi-Huei Lai, Jason Chang
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Sentence

We present a writing prototype feedback system, TellMeWhy, to provide explanations of errors in submitted essays. In our approach, the sentence with corrections is analyzed to identify error types and problem words, aimed at customizing explanations based on the context of the error. The method involves learning the relation of errors and problem words, generating common feedback patterns, and extracting grammar patterns, collocations and example sentences. At run-time, a sentence with corrections is classified, and the problem word and template are identified to provide detailed explanations. Preliminary evaluation shows that the method has potential to improve existing commercial writing services.
Reasoning Over Paths via Knowledge Base Completion
Authors: Saatviga Sudhahar, Ian Roberts, Andrea Pierleoni
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Knowledge Base Completion, Knowledge Graphs

Reasoning over paths in large scale knowledge graphs is an important problem for many applications. In this paper we discuss a simple approach to automatically build and rank paths between a source and target entity pair with learned embeddings using a knowledge base completion model (KBC). We assembled a knowledge graph by mining the available biomedical scientific literature and extracted a set of high frequency paths to use for validation. We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph. For a given entity pair we are able to reconstruct the highest ranking path 60% of the time within the the top 10 ranked paths and achieve 49% mean average precision. Our approach is compositional since any KBC model that can produce vector representations of entities can be used.
Variations of Genetic Algorithms
Authors: Alison Jenkins, Vinika Gupta, Alexis Myrick, Mary Lenoir
Date: 2019-11-01 00:00:00

The goal of this project is to develop the Genetic Algorithms (GA) for solving the Schaffer F6 function in fewer than 4000 function evaluations on a total of 30 runs. Four types of Genetic Algorithms (GA) are presented - Generational GA (GGA), Steady-State (mu+1)-GA (SSGA), Steady-Generational (mu,mu)-GA (SGGA), and (mu+mu)-GA.
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
Authors: Ming Tu, Kevin Huang, Guangtao Wang, Jing Huang, Xiaodong He, Bo-Wen Zhou
Date: 2019-11-01 00:00:00
Tasks: Learning-To-Rank, Multi-Hop Reading Comprehension, Multi-Task Learning, Prediction, Reading Comprehension, Sentence

Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences. In this paper, we propose an effective and interpretable Select, Answer and Explain (SAE) system to solve the multi-document RC problem. Our system first filters out answer-unrelated documents and thus reduce the amount of distraction information. This is achieved by a document classifier trained with a novel pairwise learning-to-rank loss. The selected answer-related documents are then input to a model to jointly predict the answer and supporting sentences. The model is optimized with a multi-task learning objective on both token level for answer prediction and sentence level for supporting sentences prediction, together with an attention-based interaction between these two tasks. Evaluated on HotpotQA, a challenging multi-hop RC data set, the proposed SAE system achieves top competitive performance in distractor setting compared to other existing systems on the leaderboard.
Explanation by Progressive Exaggeration
Authors: Sumedha Singla, Brian Pollack, Junxiang Chen, Kayhan Batmanghelich
Date: 2019-11-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Feature Importance, General Classification, Negation

As machine learning methods see greater adoption and implementation in high stakes applications such as medical image diagnosis, the need for model interpretability and explanation has become more critical. Classical approaches that assess feature importance (e.g. saliency maps) do not explain how and why a particular region of an image is relevant to the prediction. We propose a method that explains the outcome of a classification black-box by gradually exaggerating the semantic effect of a given class. Given a query input to a classifier, our method produces a progressive set of plausible variations of that query, which gradually changes the posterior probability from its original class to its negation. These counter-factually generated samples preserve features unrelated to the classification decision, such that a user can employ our method as a "tuning knob" to traverse a data manifold while crossing the decision boundary. Our method is model agnostic and only requires the output value and gradient of the predictor with respect to its input.
Toward a Better Monitoring Statistic for Profile Monitoring via Variational Autoencoders
Authors: Nurettin Sergin, Hao Yan
Date: 2019-11-01 00:00:00

Wide accessibility of imaging and profile sensors in modern industrial systems created an abundance of high-dimensional sensing variables. This led to a a growing interest in the research of high-dimensional process monitoring. However, most of the approaches in the literature assume the in-control population to lie on a linear manifold with a given basis (i.e., spline, wavelet, kernel, etc) or an unknown basis (i.e., principal component analysis and its variants), which cannot be used to efficiently model profiles with a nonlinear manifold which is common in many real-life cases. We propose deep probabilistic autoencoders as a viable unsupervised learning approach to model such manifolds. To do so, we formulate nonlinear and probabilistic extensions of the monitoring statistics from classical approaches as the expected reconstruction error (ERE) and the KL-divergence (KLD) based monitoring statistics. Through extensive simulation study, we provide insights on why latent-space based statistics are unreliable and why residual-space based ones typically perform much better for deep learning based approaches. Finally, we demonstrate the superiority of deep probabilistic models via both simulation study and a real-life case study involving images of defects from a hot steel rolling process.
BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understanding
Authors: Emad Elwany, Dave Moore, Gaurav Oberoi
Date: 2019-11-01 00:00:00
Proceeding: NeurIPS Workshop Document_Intelligen 2019 12

Fine-tuning language models, such as BERT, on domain specific corpora has proven to be valuable in domains like scientific papers and biomedical text. In this paper, we show that fine-tuning BERT on legal documents similarly provides valuable improvements on NLP tasks in the legal domain. Demonstrating this outcome is significant for analyzing commercial agreements, because obtaining large legal corpora is challenging due to their confidential nature. As such, we show that having access to large legal corpora is a competitive advantage for commercial applications, and academic research on analyzing contracts.
Progressive Compressed Records: Taking a Byte out of Deep Learning Data
Authors: Michael Kuchnik, George Amvrosiadis, Virginia Smith
Date: 2019-11-01 00:00:00

Deep learning accelerators efficiently train over vast and growing amounts of data, placing a newfound burden on commodity networks and storage devices. A common approach to conserve bandwidth involves resizing or compressing data prior to training. We introduce Progressive Compressed Records (PCRs), a data format that uses compression to reduce the overhead of fetching and transporting data, effectively reducing the training time required to achieve a target accuracy. PCRs deviate from previous storage formats by combining progressive compression with an efficient storage layout to view a single dataset at multiple fidelities---all without adding to the total dataset size. We implement PCRs and evaluate them on a range of datasets, training tasks, and hardware architectures. Our work shows that: (i) the amount of compression a dataset can tolerate exceeds 50% of the original encoding for many DL training tasks; (ii) it is possible to automatically and efficiently select appropriate compression levels for a given task; and (iii) PCRs enable tasks to readily access compressed data at runtime---utilizing as little as half the training bandwidth and thus potentially doubling training speed.
Explaining black box decisions by Shapley cohort refinement
Authors: Masayoshi Mase, Art B. Owen, Benjamin Seiler
Date: 2019-11-01 00:00:00

We introduce a variable importance measure to quantify the impact of individual input variables to a black box function. Our measure is based on the Shapley value from cooperative game theory. Many measures of variable importance operate by changing some predictor values with others held fixed, potentially creating unlikely or even logically impossible combinations. Our cohort Shapley measure uses only observed data points. Instead of changing the value of a predictor we include or exclude subjects similar to the target subject on that predictor to form a similarity cohort. Then we apply Shapley value to the cohort averages. We connect variable importance measures from explainable AI to function decompositions from global sensitivity analysis. We introduce a squared cohort Shapley value that splits previously studied Shapley effects over subjects, consistent with a Shapley axiom.
ARSM Gradient Estimator for Supervised Learning to Rank
Authors: Siamak Zamani Dadaneh, Shahin Boluki, Mingyuan Zhou, Xiaoning Qian
Date: 2019-11-01 00:00:00
Tasks: Learning-To-Rank

We propose a new model for supervised learning to rank. In our model, the relevance labels are assumed to follow a categorical distribution whose probabilities are constructed based on a scoring function. We optimize the training objective with respect to the multivariate categorical variables with an unbiased and low-variance gradient estimator. Learning-to-rank methods can generally be categorized into pointwise, pairwise, and listwise approaches. Although our scoring function is pointwise, the proposed framework permits flexibility over the choice of the loss function. In our new model, the loss function need not be differentiable and can either be pointwise or listwise. Our proposed method achieves better or comparable results on two datasets compared with existing pairwise and listwise methods.