pwc-archive/papers-with-abstracts huggingface dataset:
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop
Authors: Rujun Han, Michael Gill, Arthur Spirling, Kyunghyun Cho
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Two-sample testing, Word Embeddings

Conventional word embedding models do not leverage information from document meta-data, and they do not model uncertainty. We address these concerns with a model that incorporates document covariates to estimate conditional word embedding distributions. Our model allows for (a) hypothesis tests about the meanings of terms, (b) assessments as to whether a word is near or far from another conditioned on different covariate values, and (c) assessments as to whether estimated differences are statistically significant.
Sanskrit Sandhi Splitting using seq2(seq)2
Authors: Rahul Aralikatte, Neelamadhav Gantayat, Naveen Panwar, Anush Sankaran, Senthil Mani
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Chinese Word Segmentation, Decoder

In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi. Sandhi splitting is the process of splitting a given compound word into its constituent morphemes. Although rules governing word splitting exists in the language, it is highly challenging to identify the location of the splits in a compound word. Though existing Sandhi splitting systems incorporate these pre-defined splitting rules, they have a low accuracy as the same compound word might be broken down in multiple ways to provide syntactically correct splits. In this research, we propose a novel deep learning architecture called Double Decoder RNN (DD-RNN), which (i) predicts the location of the split(s) with 95{\%} accuracy, and (ii) predicts the constituent words (learning the Sandhi splitting rules) with 79.5{\%} accuracy, outperforming the state-of-art by 20{\%}. Additionally, we show the generalization capability of our deep learning model, by showing competitive results in the problem of Chinese word segmentation, as well.
LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNs
Authors: Daniel Kondratyuk, Tom{\'a}{\v{s}} Gaven{\v{c}}iak, Milan Straka, Jan Haji{\v{c}}
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Lemmatization, Machine Translation, Part-Of-Speech Tagging, Semantic Role Labeling, Sentiment Analysis, TAG

We present LemmaTag, a featureless neural network architecture that jointly generates part-of-speech tags and lemmas for sentences by using bidirectional RNNs with character-level and word-level embeddings. We demonstrate that both tasks benefit from sharing the encoding part of the network, predicting tag subcategories, and using the tagger output as an input to the lemmatizer. We evaluate our model across several languages with complex morphology, which surpasses state-of-the-art accuracy in both part-of-speech tagging and lemmatization in Czech, German, and Arabic.
Recovering Missing Characters in Old Hawaiian Writing
Authors: Brendan Shillingford, Oiwi Parker Jones
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Language Modeling, Language Modelling, Reading Comprehension, Transliteration

In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops. These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation. However, transliterating between older and newer texts is a laborious task when performed manually. We introduce two related methods to help solve this transliteration problem automatically. One approach is implemented, end-to-end, using finite state transducers (FSTs). The other is a hybrid deep learning approach, which approximately composes an FST with a recurrent neural network language model.
When data permutations are pathological: the case of neural natural language inference
Authors: Natalie Schluter, Daniel Varab
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Natural Language Inference

Consider two competitive machine learning models, one of which was considered state-of-the art, and the other a competitive baseline. Suppose that by just permuting the examples of the training set, say by reversing the original order, by shuffling, or by mini-batching, you could report substantially better/worst performance for the system of your choice, by multiple percentage points. In this paper, we illustrate this scenario for a trending NLP task: Natural Language Inference (NLI). We show that for the two central NLI corpora today, the learning process of neural systems is far too sensitive to permutations of the data. In doing so we reopen the question of how to judge a good neural architecture for NLI, given the available dataset and perhaps, further, the soundness of the NLI task itself in its current state.
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification
Authors: Jing Chen, Qingcai Chen, Xin Liu, Haijun Yang, Daohe Lu, Buzhou Tang
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Clustering, Paraphrase Identification, Question Answering, Semantic Textual Similarity, Sentence

This paper introduces the Bank Question (BQ) corpus, a Chinese corpus for sentence semantic equivalence identification (SSEI). The BQ corpus contains 120,000 question pairs from 1-year online bank custom service logs. To efficiently process and annotate questions from such a large scale of logs, this paper proposes a clustering based annotation method to achieve questions with the same intent. First, the deduplicated questions with the same answer are clustered into stacks by the Word Mover{'}s Distance (WMD) based Affinity Propagation (AP) algorithm. Then, the annotators are asked to assign the clustered questions into different intent categories. Finally, the positive and negative question pairs for SSEI are selected in the same intent category and between different intent categories respectively. We also present six SSEI benchmark performance on our corpus, including state-of-the-art algorithms. As the largest manually annotated public Chinese SSEI corpus in the bank domain, the BQ corpus is not only useful for Chinese question semantic matching research, but also a significant resource for cross-lingual and cross-domain SSEI research. The corpus is available in public.
Modeling Input Uncertainty in Neural Network Dependency Parsing
Authors: Rob van der Goot, Gertjan van Noord
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dependency Parsing, Lexical Normalization, Word Embeddings

Recently introduced neural network parsers allow for new approaches to circumvent data sparsity issues by modeling character level information and by exploiting raw data in a semi-supervised setting. Data sparsity is especially prevailing when transferring to non-standard domains. In this setting, lexical normalization has often been used in the past to circumvent data sparsity. In this paper, we investigate whether these new neural approaches provide similar functionality as lexical normalization, or whether they are complementary. We provide experimental results which show that a separate normalization component improves performance of a neural network parser even if it has access to character level information as well as external word embeddings. Further improvements are obtained by a straightforward but novel approach in which the top-N best candidates provided by the normalization component are available to the parser.
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
Authors: Pawe{\l} Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, I{\~n}igo Casanueva, Stefan Ultes, Osman Ramadan, Milica Ga{\v{s}}i{\'c}
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decision Making, Dialogue Management, Response Generation, Speech Recognition, Task-Oriented Dialogue Systems, Text Generation

Even though machine learning has become the major scene in dialogue research community, the real breakthrough has been blocked by the scale of data available.To address this fundamental obstacle, we introduce the Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.At a size of 10k dialogues, it is at least one order of magnitude larger than all previous annotated task-oriented corpora.The contribution of this work apart from the open-sourced dataset is two-fold:firstly, a detailed description of the data collection procedure along with a summary of data structure and analysis is provided. The proposed data-collection pipeline is entirely based on crowd-sourcing without the need of hiring professional annotators;secondly, a set of benchmark results of belief tracking, dialogue act and response generation is reported, which shows the usability of the data and sets a baseline for future studies.
Phrase-Based \& Neural Unsupervised Machine Translation
Authors: Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, Marc{'}Aurelio Ranzato
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Denoising, Machine Translation, NMT, Sentence, Translation, Unsupervised Machine Translation

Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. This work investigates how to learn to translate when having access to only large monolingual corpora in each language. We propose two model variants, a neural and a phrase-based model. Both versions leverage a careful initialization of the parameters, the denoising effect of language models and automatic generation of parallel data by iterative back-translation. These models are significantly better than methods from the literature, while being simpler and having fewer hyper-parameters. On the widely used WMT{'}14 English-French and WMT{'}16 German-English benchmarks, our models respectively obtain 28.1 and 25.2 BLEU points without using a single parallel sentence, outperforming the state of the art by more than 11 BLEU points. On low-resource languages like English-Urdu and English-Romanian, our methods achieve even better results than semi-supervised and supervised approaches leveraging the paucity of available bitexts. Our code for NMT and PBSMT is publicly available.
SyntaViz: Visualizing Voice Queries through a Syntax-Driven Hierarchical Ontology
Authors: Md. Iftekhar Tanveer, Ferhan Ture
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Natural Language Queries, Sentiment Analysis, Topic Models

This paper describes SyntaViz, a visualization interface specifically designed for analyzing natural-language queries that were created by users of a voice-enabled product. SyntaViz provides a platform for browsing the ontology of user queries from a syntax-driven perspective, providing quick access to high-impact failure points of the existing intent understanding system and evidence for data-driven decisions in the development cycle. A case study on Xfinity X1 (a voice-enabled entertainment platform from Comcast) reveals that SyntaViz helps developers identify multiple action items in a short amount of time without any special training. SyntaViz has been open-sourced for the benefit of the community.
Data2Text Studio: Automated Text Generation from Structured Data
Authors: Longxu Dou, Guanghui Qin, Jinpeng Wang, Jin-Ge Yao, Chin-Yew Lin
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Data-to-Text Generation, Text Generation

Data2Text Studio is a platform for automated text generation from structured data. It is equipped with a Semi-HMMs model to extract high-quality templates and corresponding trigger conditions from parallel data automatically, which improves the interactivity and interpretability of the generated text. In addition, several easy-to-use tools are provided for developers to edit templates of pre-trained models, and APIs are released for developers to call the pre-trained model to generate texts in third-party applications. We conduct experiments on RotoWire datasets for template extraction and text generation. The results show that our model achieves improvements on both tasks.
MorAz: an Open-source Morphological Analyzer for Azerbaijani Turkish
Authors: Berke {\"O}zen{\c{c}}, Razieh Ehsani, Ercan Solak
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Morphological Analysis

MorAz is an open-source morphological analyzer for Azerbaijani Turkish. The analyzer is available through both as a website for interactive exploration and as a RESTful web service for integration into a natural language processing pipeline. MorAz implements the morphology of Azerbaijani Turkish in two-level using Helsinki finite-state transducer and wraps the analyzer with python scripts in a Django instance.
An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering LSTM
Authors: Ekaterina Loginova, G{\"u}nter Neumann
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Feature Engineering, Machine Translation, Question Answering

We present a visualisation tool which aims to illuminate the inner workings of an LSTM model for question answering. It plots heatmaps of neurons{'} firings and allows a user to check the dependency between neurons and manual features. The system possesses an interactive web-interface and can be adapted to other models and domains.
Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension
Authors: Shusen Liu, Tao Li, Zhimin Li, Vivek Srikumar, Valerio Pascucci, Peer-Timo Bremer
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Decision Making, Natural Language Inference, Reading Comprehension

Neural networks models have gained unprecedented popularity in natural language processing due to their state-of-the-art performance and the flexible end-to-end training scheme. Despite their advantages, the lack of interpretability hinders the deployment and refinement of the models. In this work, we present a flexible visualization library for creating customized visual analytic environments, in which the user can investigate and interrogate the relationships among the input, the model internals (i.e., attention), and the output predictions, which in turn shed light on the model decision-making process.
DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction
Authors: Heike Adel, Laura Ana Maria Bostan, Sean Papay, Sebastian Pad{\'o}, Roman Klinger
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: BIG-bench Machine Learning, Relation, Relation Extraction, Semantic Role Labeling, Sentiment Analysis, slot-filling, Slot Filling, Transfer Learning

Most machine learning systems for natural language processing are tailored to specific tasks. As a result, comparability of models across tasks is missing and their applicability to new tasks is limited. This affects end users without machine learning experience as well as model developers. To address these limitations, we present DERE, a novel framework for declarative specification and compilation of template-based information extraction. It uses a generic specification language for the task and for data annotations in terms of spans and frames. This formalism enables the representation of a large variety of natural language processing challenges. The backend can be instantiated by different models, following different paradigms. The clear separation of frame specification and model backend will ease the implementation of new models and the evaluation of different models across different tasks. Furthermore, it simplifies transfer learning, joint learning across tasks and/or domains as well as the assessment of model generalizability. DERE is available as open-source software.
Juman++: A Morphological Analysis Toolkit for Scriptio Continua
Authors: Arseny Tolmachev, Daisuke Kawahara, Sadao Kurohashi
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Art Analysis, Language Modeling, Language Modelling, Morphological Analysis, Part-Of-Speech Tagging

We present a three-part toolkit for developing morphological analyzers for languages without natural word boundaries. The first part is a C++11/14 lattice-based morphological analysis library that uses a combination of linear and recurrent neural net language models for analysis. The other parts are a tool for exposing problems in the trained model and a partial annotation tool. Our morphological analyzer of Japanese achieves new SOTA on Jumandic-based corpora while being 250 times faster than the previous one. We also perform a small experiment and quantitive analysis and experience of using development tools. All components of the toolkit is open source and available under a permissive Apache 2 License.
Visualization of the Topic Space of Argument Search Results in args.me
Authors: Yamen Ajjour, Henning Wachsmuth, Dora Kiesel, Patrick Riehmann, Fan Fan, Giuliano Castiglia, Rosemary Adejoh, Bernd Fr{\"o}hlich, Benno Stein
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Topic Models

In times of fake news and alternative facts, pro and con arguments on controversial topics are of increasing importance. Recently, we presented args.me as the first search engine for arguments on the web. In its initial version, args.me ranked arguments solely by their relevance to a topic queried for, making it hard to learn about the diverse topical aspects covered by the search results. To tackle this shortcoming, we integrated a visualization interface for result exploration in args.me that provides an instant overview of the main aspects in a barycentric coordinate system. This topic space is generated ad-hoc from controversial issues on Wikipedia and argument-specific LDA models. In two case studies, we demonstrate how individual arguments can be found easily through interactions with the visualization, such as highlighting and filtering.
CogCompTime: A Tool for Understanding Time in Natural Language
Authors: Qiang Ning, Ben Zhou, Zhili Feng, Haoruo Peng, Dan Roth
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Natural Language Understanding, Question Answering

Automatic extraction of temporal information is important for natural language understanding. It involves two basic tasks: (1) Understanding time expressions that are mentioned explicitly in text (e.g., February 27, 1998 or tomorrow), and (2) Understanding temporal information that is conveyed implicitly via relations. This paper introduces CogCompTime, a system that has these two important functionalities. It incorporates the most recent progress, achieves state-of-the-art performance, and is publicly available at \url{http://cogcomp.org/page/publication_view/844}.
Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition
Authors: Jan-Thorsten Peter, Eugen Beck, Hermann Ney
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, Translation

Training and testing many possible parameters or model architectures of state-of-the-art machine translation or automatic speech recognition system is a cumbersome task. They usually require a long pipeline of commands reaching from pre-processing the training data to post-processing and evaluating the output.
Visualizing Group Dynamics based on Multiparty Meeting Understanding
Authors: Ni Zhang, Tongtao Zhang, Indrani Bhattacharya, Heng Ji, Rich Radke
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Decision Making, Opinion Mining, Speech Recognition

Group discussions are usually aimed at sharing opinions, reaching consensus and making good decisions based on group knowledge. During a discussion, participants might adjust their own opinions as well as tune their attitudes towards others{'} opinions, based on the unfolding interactions. In this paper, we demonstrate a framework to visualize such dynamics; at each instant of a conversation, the participants{'} opinions and potential influence on their counterparts is easily visualized. We use multi-party meeting opinion mining based on bipartite graphs to extract opinions and calculate mutual influential factors, using the Lunar Survival Task as a study case.
An Interface for Annotating Science Questions
Authors: Michael Boratko, Harshit Padigela, Divyendra Mikkilineni, Pritish Yuvraj, Rajarshi Das, Andrew McCallum, Maria Chang, Achille Fokoue, Pavan Kapanipathi, Nicholas Mattei, Ryan Musa, Kartik Talamadupula, Michael Witbrock
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: AI2 Reasoning Challenge, ARC, General Classification, Information Retrieval

Recent work introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set and a Challenge Set. That work includes an analysis of 100 questions with respect to the types of knowledge and reasoning required to answer them. However, it does not include clear definitions of these types, nor does it offer information about the quality of the labels or the annotation process used. In this paper, we introduce a novel interface for human annotation of science question-answer pairs with their respective knowledge and reasoning types, in order that the classification of new questions may be improved. We build on the classification schema proposed by prior work on the ARC dataset, and evaluate the effectiveness of our interface with a preliminary study involving 10 participants.
APLenty: annotation tool for creating high-quality datasets using active and proactive learning
Authors: Minh-Quoc Nghiem, Sophia Ananiadou
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Active Learning, Multi-Label Learning, Part-Of-Speech Tagging

In this paper, we present APLenty, an annotation tool for creating high-quality sequence labeling datasets using active and proactive learning. A major innovation of our tool is the integration of automatic annotation with active learning and proactive learning. This makes the task of creating labeled datasets easier, less time-consuming and requiring less human effort. APLenty is highly flexible and can be adapted to various other tasks.
Interactive Instance-based Evaluation of Knowledge Base Question Answering
Authors: Daniil Sorokin, Iryna Gurevych
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Entity Linking, Knowledge Base Question Answering, Question Answering, Semantic Parsing

Most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we present a tool that aids in debugging of question answering systems that construct a structured semantic representation for the input question. Previous work has largely focused on building question answering interfaces or evaluation frameworks that unify multiple data sets. The primary objective of our system is to enable interactive debugging of model predictions on individual instances (questions) and to simplify manual error analysis. Our interactive interface helps researchers to understand the shortcomings of a particular model, qualitatively analyze the complete pipeline and compare different models. A set of sit-by sessions was used to validate our interface design.