pwc-archive/papers-with-abstracts huggingface dataset:
Integrating Multiple NLP Technologies into an Open-source Platform for Multilingual Media Monitoring
Authors: Ulrich Germann, Ren{\=a}rs Liepins, Didzis Gosko, Guntis Barzdins
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Automatic Speech Recognition (ASR), Machine Translation, Speech Recognition

The open-source SUMMA Platform is a highly scalable distributed architecture for monitoring a large number of media broadcasts in parallel, with a lag behind actual broadcast time of at most a few minutes. It assembles numerous state-of-the-art NLP technologies into a fully automated media ingestion pipeline that can record live broadcasts, detect and transcribe spoken content, translate from several languages (original text or transcribed speech) into English, recognize Named Entities, detect topics, cluster and summarize documents across language barriers, and extract and store factual claims in these news items. This paper describes the intended use cases and discusses the system design decisions that allowed us to integrate state-of-the-art NLP modules into an effective workflow with comparatively little effort.
The Annotated Transformer
Authors: Alex Rush, er
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7

A major goal of open-source NLP is to quickly and accurately reproduce the results of new work, in a manner that the community can easily use and modify. While most papers publish enough detail for replication, it still may be difficult to achieve good results in practice. This paper presents a worked exercise of paper reproduction with the goal of implementing the results of the recent Transformer model. The replication exercise aims at simple code structure that follows closely with the original work, while achieving an efficient usable system.
Systematic Error Analysis of the Stanford Question Answering Dataset
Authors: Marc-Antoine Rondeau, T. J. Hazen
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Common Sense Reasoning, Machine Reading Comprehension, Question Answering, Reading Comprehension

We analyzed the outputs of multiple question answering (QA) models applied to the Stanford Question Answering Dataset (SQuAD) to identify the core challenges for QA systems on this data set. Through an iterative process, challenging aspects were hypothesized through qualitative analysis of the common error cases. A classifier was then constructed to predict whether SQuAD test examples were likely to be difficult for systems to answer based on features associated with the hypothesized aspects. The classifier{'}s performance was used to accept or reject each aspect as an indicator of difficulty. With this approach, we ensured that our hypotheses were systematically tested and not simply accepted based on our pre-existing biases. Our explanations are not accepted based on human evaluation of individual examples. This process also enabled us to identify the primary QA strategy learned by the models, i.e., systems determined the acceptable answer type for a question and then selected the acceptable answer span of that type containing the highest density of words present in the question within its local vicinity in the passage.
A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension
Authors: Seunghak Yu, Sathish Reddy Indurthi, Seohyun Back, Haejun Lee
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Machine Reading Comprehension, Question Answering, Reading Comprehension, TriviaQA

Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing. In recent years, several end-to-end neural network models have been proposed to solve RC tasks. However, most of these models suffer in reasoning over long documents. In this work, we propose a novel Memory Augmented Machine Comprehension Network (MAMCN) to address long-range dependencies present in machine reading comprehension. We perform extensive experiments to evaluate proposed method with the renowned benchmark datasets such as SQuAD, QUASAR-T, and TriviaQA. We achieve the state of the art performance on both the document-level (QUASAR-T, TriviaQA) and paragraph-level (SQuAD) datasets compared to all the previously published approaches.
Tackling Adversarial Examples in QA via Answer Sentence Selection
Authors: Yuanhang Ren, Ye Du, Di Wang
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Articles, Question Answering, Reading Comprehension, Sentence

Question answering systems deteriorate dramatically in the presence of adversarial sentences in articles. According to Jia and Liang (2017), the single BiDAF system (Seo et al., 2016) only achieves an F1 score of 4.8 on the ADDANY adversarial dataset. In this paper, we present a method to tackle this problem via answer sentence selection. Given a paragraph of an article and a corresponding query, instead of directly feeding the whole paragraph to the single BiDAF system, a sentence that most likely contains the answer to the query is first selected, which is done via a deep neural network based on TreeLSTM (Tai et al., 2015). Experiments on ADDANY adversarial dataset validate the effectiveness of our method. The F1 score has been improved to 52.3.
RECIPE: Applying Open Domain Question Answering to Privacy Policies
Authors: Yan Shvartzshanider, Ananth Balashankar, Thomas Wies, Lakshminarayanan Subramanian
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Descriptive, Open-Domain Question Answering, Question Answering, Reading Comprehension

We describe our experiences in using an open domain question answering model (Chen et al., 2017) to evaluate an out-of-domain QA task of assisting in analyzing privacy policies of companies. Specifically, Relevant CI Parameters Extractor (RECIPE) seeks to answer questions posed by the theory of contextual integrity (CI) regarding the information flows described in the privacy statements. These questions have a simple syntactic structure and the answers are factoids or descriptive in nature. The model achieved an F1 score of 72.33, but we noticed that combining the results of this model with a neural dependency parser based approach yields a significantly higher F1 score of 92.35 compared to manual annotations. This indicates that future work which in-corporates signals from parsing like NLP tasks more explicitly can generalize better on out-of-domain tasks.
Neural Models for Key Phrase Extraction and Question Generation
Authors: S Subramanian, eep, Tong Wang, Xingdi Yuan, Saizheng Zhang, Adam Trischler, Yoshua Bengio
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Question Answering, Question Generation, Question-Generation, Reading Comprehension

We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.
Iterative Back-Translation for Neural Machine Translation
Authors: Vu Cong Duy Hoang, Philipp Koehn, Gholamreza Haffari, Trevor Cohn
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Machine Translation, Translation

We present iterative back-translation, a method for generating increasingly better synthetic parallel data from monolingual data to train neural machine translation systems. Our proposed method is very simple yet effective and highly applicable in practice. We demonstrate improvements in neural machine translation quality in both high and low resourced scenarios, including the best reported BLEU scores for the WMT 2017 Germanâ†”English tasks.
Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation
Authors: Huda Khayrallah, Brian Thompson, Kevin Duh, Philipp Koehn
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Domain Adaptation, Machine Translation, NMT, Translation

Supervised domain adaptation{---}where a large generic corpus and a smaller in-domain corpus are both available for training{---}is a challenge for neural machine translation (NMT). Standard practice is to train a generic model and use it to initialize a second model, then continue training the second model on in-domain data to produce an in-domain model. We add an auxiliary term to the training objective during continued training that minimizes the cross entropy between the in-domain model{'}s output word distribution and that of the out-of-domain model to prevent the model{'}s output from differing too much from the original out-of-domain model. We perform experiments on EMEA (descriptions of medicines) and TED (rehearsed presentations), initialized from a general domain (WMT) model. Our method shows improvements over standard continued training by up to 1.5 BLEU.
Enhancement of Encoder and Attention Using Target Monolingual Corpora in Neural Machine Translation
Authors: Kenji Imamura, Atsushi Fujita, Eiichiro Sumita
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Decoder, Diversity, Language Modeling, Language Modelling, Machine Translation, Sentence, Translation

A large-scale parallel corpus is required to train encoder-decoder neural machine translation. The method of using synthetic parallel texts, in which target monolingual corpora are automatically translated into source sentences, is effective in improving the decoder, but is unreliable for enhancing the encoder. In this paper, we propose a method that enhances the encoder and attention using target monolingual corpora by generating multiple source sentences via sampling. By using multiple source sentences, diversity close to that of humans is achieved. Our experimental results show that the translation quality is improved by increasing the number of synthetic source sentences for each given target sentence, and quality close to that using a manually created parallel corpus was achieved.
Document-Level Adaptation for Neural Machine Translation
Authors: Sachith Sri Ram Kothur, Rebecca Knowles, Philipp Koehn
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Machine Translation, Sentence, Translation, Word Translation

It is common practice to adapt machine translation systems to novel domains, but even a well-adapted system may be able to perform better on a particular document if it were to learn from a translator{'}s corrections within the document itself. We focus on adaptation within a single document {--} appropriate for an interactive translation scenario where a model adapts to a human translator{'}s input over the course of a document. We propose two methods: single-sentence adaptation (which performs online adaptation one sentence at a time) and dictionary adaptation (which specifically addresses the issue of translating novel words). Combining the two models results in improvements over both approaches individually, and over baseline systems, even on short documents. On WMT news test data, we observe an improvement of +1.8 BLEU points and +23.3{\%} novel word translation accuracy and on EMEA data (descriptions of medications) we observe an improvement of +2.7 BLEU points and +49.2{\%} novel word translation accuracy.
NICT Self-Training Approach to Neural Machine Translation at NMT-2018
Authors: Kenji Imamura, Eiichiro Sumita
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Machine Translation, NMT, Translation

This paper describes the NICT neural machine translation system submitted at the NMT-2018 shared task. A characteristic of our approach is the introduction of self-training. Since our self-training does not change the model structure, it does not influence the efficiency of translation, such as the translation speed. The experimental results showed that the translation quality improved not only in the sequence-to-sequence (seq-to-seq) models but also in the transformer models.
OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU
Authors: Jean Senellart, Dakun Zhang, Bo wang, Guillaume Klein, Ramatch, Jean-Pierre irin, Josep Crego, Alex Rush, er
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: CPU, Machine Translation, Neural Architecture Search, NMT, Quantization, Translation

We present a system description of the OpenNMT Neural Machine Translation entry for the WNMT 2018 evaluation. In this work, we developed a heavily optimized NMT inference model targeting a high-performance CPU system. The final system uses a combination of four techniques, all of them lead to significant speed-ups in combination: (a) sequence distillation, (b) architecture modifications, (c) precomputation, particularly of vocabulary, and (d) CPU targeted quantization. This work achieves the fastest performance of the shared task, and led to the development of new features that have been integrated to OpenNMT and available to the community.
Predicting Brain Activation with WordNet Embeddings
Authors: Jo{\~a}o Ant{\'o}nio Rodrigues, Ruben Branco, Jo{\~a}o Silva, Chakaveh Saedi, Ant{\'o}nio Branco
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Word Embeddings

The task of taking a semantic representation of a noun and predicting the brain activity triggered by it in terms of fMRI spatial patterns was pioneered by Mitchell et al. 2008. That seminal work used word co-occurrence features to represent the meaning of the nouns. Even though the task does not impose any specific type of semantic representation, the vast majority of subsequent approaches resort to feature-based models or to semantic spaces (aka word embeddings). We address this task, with competitive results, by using instead a semantic network to encode lexical semantics, thus providing further evidence for the cognitive plausibility of this approach to model lexical meaning.
Do Speakers Produce Discourse Connectives Rationally?
Authors: Frances Yung, Vera Demberg
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Informativeness

A number of different discourse connectives can be used to mark the same discourse relation, but it is unclear what factors affect connective choice. One recent account is the Rational Speech Acts theory, which predicts that speakers try to maximize the informativeness of an utterance such that the listener can interpret the intended meaning correctly. Existing prior work uses referential language games to test the rational account of speakers{'} production of concrete meanings, such as identification of objects within a picture. Building on the same paradigm, we design a novel Discourse Continuation Game to investigate speakers{'} production of abstract discourse relations. Experimental results reveal that speakers significantly prefer a more informative connective, in line with predictions of the RSA model.
Language Production Dynamics with Recurrent Neural Networks
Authors: Jes{\'u}s Calvillo, Matthew Crocker
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Caption Generation, Language Modeling, Language Modelling, Machine Translation, Sentence, Translation

We present an analysis of the internal mechanism of the recurrent neural model of sentence production presented by Calvillo et al. (2016). The results show clear patterns of computation related to each layer in the network allowing to infer an algorithmic account, where the semantics activates the semantically related words, then each word generated at each time step activates syntactic and semantic constraints on possible continuations, while the recurrence preserves information through time. We propose that such insights could generalize to other models with similar architecture, including some used in computational linguistics for language modeling, machine translation and image caption generation.
Multi-glance Reading Model for Text Understanding
Authors: Pengcheng Zhu, Yujiu Yang, Wenqiang Gao, Yi Liu
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Document Classification, Machine Translation, model, Reading Comprehension, Sentiment Analysis

In recent years, a variety of recurrent neural networks have been proposed, e.g LSTM. However, existing models only read the text once, it cannot describe the situation of repeated reading in reading comprehension. In fact, when reading or analyzing a text, we may read the text several times rather than once if we couldn{'}t well understand it. So, how to model this kind of the reading behavior? To address the issue, we propose a multi-glance mechanism (MGM) for modeling the habit of reading behavior. In the proposed framework, the actual reading process can be fully simulated, and then the obtained information can be consistent with the task. Based on the multi-glance mechanism, we design two types of recurrent neural network models for repeated reading: Glance Cell Model (GCM) and Glance Gate Model (GGM). Visualization analysis of the GCM and the GGM demonstrates the effectiveness of multi-glance mechanisms. Experiments results on the large-scale datasets show that the proposed methods can achieve better performance.
Predicting Japanese Word Order in Double Object Constructions
Authors: Masayuki Asahara, Satoshi Nambu, Shin-Ichiro Sano
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Object

This paper presents a statistical model to predict Japanese word order in the double object constructions. We employed a Bayesian linear mixed model with manually annotated predicate-argument structure data. The findings from the refined corpus analysis confirmed the effects of information status of an NP as {`}givennew ordering{'} in addition to the effects of {`}long-before-short{'} as a tendency of the general Japanese word order.
Affordances in Grounded Language Learning
Authors: Stephen McGregor, Kyungtae Lim
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Grounded language learning, Question Answering, World Knowledge

We present a novel methodology involving mappings between different modes of semantic representation. We propose distributional semantic models as a mechanism for representing the kind of world knowledge inherent in the system of abstract symbols characteristic of a sophisticated community of language users. Then, motivated by insight from ecological psychology, we describe a model approximating affordances, by which we mean a language learner{'}s direct perception of opportunities for action in an environment. We present a preliminary experiment involving mapping between these two representational modalities, and propose that our methodology can become the basis for a cognitively inspired model of grounded language learning.
Rating Distributions and Bayesian Inference: Enhancing Cognitive Models of Spatial Language Use
Authors: Thomas Kluth, Holger Schultheis
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Bayesian Inference, Linguistic Acceptability

We present two methods that improve the assessment of cognitive models. The first method is applicable to models computing average acceptability ratings. For these models, we propose an extension that simulates a full rating distribution (instead of average ratings) and allows generating individual ratings. Our second method enables Bayesian inference for models generating individual data. To this end, we propose to use the cross-match test (Rosenbaum, 2005) as a likelihood function. We exemplarily present both methods using cognitive models from the domain of spatial language use. For spatial language use, determining linguistic acceptability judgments of a spatial preposition for a depicted spatial relation is assumed to be a crucial process (Logan and Sadler, 1996). Existing models of this process compute an average acceptability rating. We extend the models and {--} based on existing data {--} show that the extended models allow extracting more information from the empirical data and yield more readily interpretable information about model successes and failures. Applying Bayesian inference, we find that model performance relies less on mechanisms of capturing geometrical aspects than on mapping the captured geometry to a rating interval.
The Role of Syntax During Pronoun Resolution: Evidence from fMRI
Authors: Jixing Li, Murielle Fabre, Wen-Ming Luh, John Hale
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Coreference Resolution

The current study examined the role of syntactic structure during pronoun resolution. We correlated complexity measures derived by the syntax-sensitive Hobbs algorithm and a neural network model for pronoun resolution with brain activity of participants listening to an audiobook during fMRI recording. Compared to the neural network model, the Hobbs algorithm is associated with larger clusters of brain activation in a network including the left Broca{'}s area.
A Sound and Complete Left-Corner Parsing for Minimalist Grammars
Authors: Milo{\v{s}} Stanojevi{\'c}, Edward Stabler
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Relation

This paper presents a left-corner parser for minimalist grammars. The relation between the parser and the grammar is transparent in the sense that there is a very simple 1-1 correspondence between derivations and parses. Like left-corner context-free parsers, left-corner minimalist parsers can be non-terminating when the grammar has empty left corners, so an easily computed left-corner oracle is defined to restrict the search.
Compositional Morpheme Embeddings with Affixes as Functions and Stems as Arguments
Authors: Daniel Edmiston, Karl Stratos
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Dependency Parsing, Word Embeddings

This work introduces a novel, linguistically motivated architecture for composing morphemes to derive word embeddings. The principal novelty in the work is to treat stems as vectors and affixes as functions over vectors. In this way, our model{'}s architecture more closely resembles the compositionality of morphemes in natural language. Such a model stands in opposition to models which treat morphemes uniformly, making no distinction between stem and affix. We run this new architecture on a dependency parsing task in Korean{---}a language rich in derivational morphology{---}and compare it against a lexical baseline,along with other sub-word architectures. StAffNet, the name of our architecture, shows competitive performance with the state-of-the-art on this task.
Unsupervised Source Hierarchies for Low-Resource Neural Machine Translation
Authors: Anna Currey, Kenneth Heafield
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Low Resource Neural Machine Translation, Low-Resource Neural Machine Translation, Machine Translation, Natural Language Inference, NMT, Sentence, Sentiment Analysis, Translation

Incorporating source syntactic information into neural machine translation (NMT) has recently proven successful (Eriguchi et al., 2016; Luong et al., 2016). However, this is generally done using an outside parser to syntactically annotate the training data, making this technique difficult to use for languages or domains for which a reliable parser is not available. In this paper, we introduce an unsupervised tree-to-sequence (tree2seq) model for neural machine translation; this model is able to induce an unsupervised hierarchical structure on the source sentence based on the downstream task of neural machine translation. We adapt the Gumbel tree-LSTM of Choi et al. (2018) to NMT in order to create the encoder. We evaluate our model against sequential and supervised parsing baselines on three low- and medium-resource language pairs. For low-resource cases, the unsupervised tree2seq encoder significantly outperforms the baselines; no improvements are seen for medium-resource translation.