pwc-archive/papers-with-abstracts huggingface dataset:
Noise-Robust Morphological Disambiguation for Dialectal Arabic
Authors: Nasser Zalmout, Alex Erdmann, er, Nizar Habash
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Lexical Normalization, Morphological Analysis, Morphological Disambiguation, Morphological Tagging, Part-Of-Speech Tagging

User-generated text tends to be noisy with many lexical and orthographic inconsistencies, making natural language processing (NLP) tasks more challenging. The challenging nature of noisy text processing is exacerbated for dialectal content, where in addition to spelling and lexical differences, dialectal text is characterized with morpho-syntactic and phonetic variations. These issues increase sparsity in NLP models and reduce accuracy. We present a neural morphological tagging and disambiguation model for Egyptian Arabic, with various extensions to handle noisy and inconsistent content. Our models achieve about 5{\%} relative error reduction (1.1{\%} absolute improvement) for full morphological analysis, and around 22{\%} relative error reduction (1.8{\%} absolute improvement) for part-of-speech tagging, over a state-of-the-art baseline.
Learning Word Embeddings for Low-Resource Languages by PU Learning
Authors: Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Document Ranking, Image Captioning, Learning Word Embeddings, Named Entity Recognition (NER), Question Answering, Sentiment Analysis, Word Embeddings

Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word embedding. However, such a corpus may not be available for some low-resource languages. In this paper, we study how to effectively learn a word embedding model on a corpus with only a few million tokens. In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved. In contrast to existing approaches often only sample a few unobserved word pairs as negative samples, we argue that the zero entries in the co-occurrence matrix also provide valuable information. We then design a Positive-Unlabeled Learning (PU-Learning) approach to factorize the co-occurrence matrix and validate the proposed approaches in four different languages.
Comparing Automatic and Human Evaluation of Local Explanations for Text Classification
Authors: Dong Nguyen
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: General Classification, Recommendation Systems, text-classification, Text Classification

Text classification models are becoming increasingly complex and opaque, however for many applications it is essential that the models are interpretable. Recently, a variety of approaches have been proposed for generating local explanations. While robust evaluations are needed to drive further progress, so far it is unclear which evaluation approaches are suitable. This paper is a first step towards more robust evaluations of local explanations. We evaluate a variety of local explanation approaches using automatic measures based on word deletion. Furthermore, we show that an evaluation using a crowdsourcing experiment correlates moderately with these automatic measures and that a variety of other factors also impact the human judgements.
Specialising Word Vectors for Lexical Entailment
Authors: Ivan Vuli{\'c}, Nikola Mrk{\v{s}}i{\'c}
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Dialogue State Tracking, Lexical Entailment, Machine Translation, Natural Language Inference, Relation, Representation Learning, Semantic Similarity, Semantic Textual Similarity, Text Generation, Word Embeddings

We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymy-hypernymy pairs closer together in the transformed Euclidean space. The proposed asymmetric distance measure adjusts the norms of word vectors to reflect the actual WordNet-style hierarchy of concepts. Simultaneously, a joint objective enforces semantic similarity using the symmetric cosine distance, yielding a vector space specialised for both lexical relations at once. LEAR specialisation achieves state-of-the-art performance in the tasks of hypernymy directionality, hypernymy detection, and graded lexical entailment, demonstrating the effectiveness and robustness of the proposed asymmetric specialisation model.
A Structured Syntax-Semantics Interface for English-AMR Alignment
Authors: Ida Szubert, Adam Lopez, Nathan Schneider
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Abstract Meaning Representation, AMR Parsing

Abstract Meaning Representation (AMR) annotations are often assumed to closely mirror dependency syntax, but AMR explicitly does not require this, and the assumption has never been tested. To test it, we devise an expressive framework to align AMR graphs to dependency graphs, which we use to annotate 200 AMRs. Our annotation explains how 97{\%} of AMR edges are evoked by words or syntax. Previously existing AMR alignment frameworks did not allow for mapping AMR onto syntax, and as a consequence they explained at most 23{\%}. While we find that there are indeed many cases where AMR annotations closely mirror syntax, there are also pervasive differences. We use our annotations to test a baseline AMR-to-syntax aligner, finding that this task is more difficult than AMR-to-string alignment; and to pinpoint errors in an AMR parser. We make our data and code freely available for further research on AMR parsing and generation, and the relationship of AMR to syntax.
Early Text Classification Using Multi-Resolution Concept Representations
Authors: Adrian Pastor L{\'o}pez-Monroy, Fabio A. Gonz{\'a}lez, Manuel Montes, Hugo Jair Escalante, Thamar Solorio
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Classification, Depression Detection, General Classification, text-classification, Text Classification

The intensive use of e-communications in everyday life has given rise to new threats and risks. When the vulnerable asset is the user, detecting these potential attacks before they cause serious damages is extremely important. This paper proposes a novel document representation to improve the early detection of risks in social media sources. The goal is to effectively identify the potential risk using as few text as possible and with as much anticipation as possible. Accordingly, we devise a Multi-Resolution Representation (MulR), which allows us to generate multiple {``}views{''} of the analyzed text. These views capture different semantic meanings for words and documents at different levels of detail, which is very useful in early scenarios to model the variable amounts of evidence. Intuitively, the representation captures better the content of short documents (very early stages) in low resolutions, whereas large documents (medium/large stages) are better modeled with higher resolutions. We evaluate the proposed ideas in two different tasks where anticipation is critical: sexual predator detection and depression detection. The experimental evaluation for these early tasks revealed that the proposed approach outperforms previous methodologies by a considerable margin.
Pivot Based Language Modeling for Improved Neural Domain Adaptation
Authors: Yftah Ziser, Roi Reichart
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Domain Adaptation, Language Modeling, Language Modelling, Representation Learning, Sentiment Analysis, Sentiment Classification

Representation learning with pivot-based methods and with Neural Networks (NNs) have lead to significant progress in domain adaptation for Natural Language Processing. However, most previous work that follows these approaches does not explicitly exploit the structure of the input text, and its output is most often a single representation vector for the entire text. In this paper we present the Pivot Based Language Model (PBLM), a representation learning model that marries together pivot-based and NN modeling in a structure aware manner. Particularly, our model processes the information in the text with a sequential NN (LSTM) and its output consists of a representation vector for every input word. Unlike most previous representation learning models in domain adaptation, PBLM can naturally feed structure aware text classifiers such as LSTM and CNN. We experiment with the task of cross-domain sentiment classification on 20 domain pairs and show substantial improvements over strong baselines.
The Context-Dependent Additive Recurrent Neural Net
Authors: Quan Hung Tran, Tuan Lai, Gholamreza Haffari, Ingrid Zukerman, Trung Bui, Hung Bui
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Language Modeling, Language Modelling, Question Answering

Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP). Here, instead of relying solely on the information presented in the text, the learning agents have access to a strong external signal given to assist the learning process. In this paper, we propose a novel family of Recurrent Neural Network unit: the Context-dependent Additive Recurrent Neural Network (CARNN) that is designed specifically to address this type of problem. The experimental results on public datasets in the dialog problem (Babi dialog Task 6 and Frame), contextual language model (Switchboard and Penn Tree Bank) and question answering (Trec QA) show that our novel CARNN-based architectures outperform previous methods.
Combining Character and Word Information in Neural Machine Translation Using a Multi-Level Attention
Authors: Huadong Chen, Shu-Jian Huang, David Chiang, Xin-yu Dai, Jia-Jun Chen
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Machine Translation, Sentence, Translation

Natural language sentences, being hierarchical, can be represented at different levels of granularity, like words, subwords, or characters. But most neural machine translation systems require the sentence to be represented as a sequence at a single level of granularity. It can be difficult to determine which granularity is better for a particular translation task. In this paper, we improve the model by incorporating multiple levels of granularity. Specifically, we propose (1) an encoder with character attention which augments the (sub)word-level representation with character-level information; (2) a decoder with multiple attentions that enable the representations from different levels of granularity to control the translation cooperatively. Experiments on three translation tasks demonstrate that our proposed models outperform the standard word-based model, the subword-based model, and a strong character-based model.
Target Foresight Based Attention for Neural Machine Translation
Authors: Xintong Li, Lemao Liu, Zhaopeng Tu, Shuming Shi, Max Meng
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

In neural machine translation, an attention model is used to identify the aligned source words for a target word (target foresight word) in order to select translation context, but it does not make use of any information of this target foresight word at all. Previous work proposed an approach to improve the attention model by explicitly accessing this target foresight word and demonstrated the substantial gains in alignment task. However, this approach is useless in machine translation task on which the target foresight word is unavailable. In this paper, we propose a new attention model enhanced by the implicit information of target foresight word oriented to both alignment and translation tasks. Empirical experiments on Chinese-to-English and Japanese-to-English datasets show that the proposed attention model delivers significant improvements in terms of both alignment error rate and BLEU.
Context Sensitive Neural Lemmatization with Lematus
Authors: Toms Bergmanis, Sharon Goldwater
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Lemmatization, Machine Translation, Sentence, Word Embeddings

The main motivation for developing contextsensitive lemmatizers is to improve performance on unseen and ambiguous words. Yet previous systems have not carefully evaluated whether the use of context actually helps in these cases. We introduce Lematus, a lemmatizer based on a standard encoder-decoder architecture, which incorporates character-level sentence context. We evaluate its lemmatization accuracy across 20 languages in both a full data setting and a lower-resource setting with 10k training examples in each language. In both settings, we show that including context significantly improves results against a context-free version of the model. Context helps more for ambiguous words than for unseen words, though the latter has a greater effect on overall performance differences between languages. We also compare to three previous context-sensitive lemmatization systems, which all use pre-extracted edit trees as well as hand-selected features and/or additional sources of information such as tagged training data. Without using any of these, our context-sensitive model outperforms the best competitor system (Lemming) in the fulldata setting, and performs on par in the lowerresource setting.
Simple Models for Word Formation in Slang
Authors: Vivek Kulkarni, William Yang Wang
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6

We propose the first generative models for three types of extra-grammatical word formation phenomena abounding in slang: Blends, Clippings, and Reduplicatives. Adopting a data-driven approach coupled with linguistic knowledge, we propose simple models with state of the art performance on human annotated gold standard datasets. Overall, our models reveal insights into the generative processes of word formation in slang {--} insights which are increasingly relevant in the context of the rising prevalence of slang and non-standard varieties on the Internet
Using Morphological Knowledge in Open-Vocabulary Neural Language Models
Authors: Austin Matthews, Graham Neubig, Chris Dyer
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Language Modeling, Language Modelling, Morphological Disambiguation

Languages with productive morphology pose problems for language models that generate words from a fixed vocabulary. Although character-based models allow any possible word type to be generated, they are linguistically na{\"\i}ve: they must discover that words exist and are delimited by spaces{---}basic linguistic facts that are built in to the structure of word-based models. We introduce an open-vocabulary language model that incorporates more sophisticated linguistic knowledge by predicting words using a mixture of three generative processes: (1) by generating words as a sequence of characters, (2) by directly generating full word forms, and (3) by generating words as a sequence of morphemes that are combined using a hand-written morphological analyzer. Experiments on Finnish, Turkish, and Russian show that our model outperforms character sequence models and other strong baselines on intrinsic and extrinsic measures. Furthermore, we show that our model learns to exploit morphological knowledge encoded in the analyzer, and, as a byproduct, it can perform effective unsupervised morphological disambiguation.
A Neural Layered Model for Nested Named Entity Recognition
Authors: Meizhi Ju, Makoto Miwa, Sophia Ananiadou
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Nested Mention Recognition, Nested Named Entity Recognition, Relation Extraction

Entity mentions embedded in longer entity mentions are referred to as nested entities. Most named entity recognition (NER) systems deal only with the flat entities and ignore the inner nested ones, which fails to capture finer-grained semantic information in underlying texts. To address this issue, we propose a novel neural model to identify nested entities by dynamically stacking flat NER layers. Each flat NER layer is based on the state-of-the-art flat NER model that captures sequential context representation with bidirectional Long Short-Term Memory (LSTM) layer and feeds it to the cascaded CRF layer. Our model merges the output of the LSTM layer in the current flat NER layer to build new representation for detected entities and subsequently feeds them into the next flat NER layer. This allows our model to extract outer entities by taking full advantage of information encoded in their corresponding inner entities, in an inside-to-outside way. Our model dynamically stacks the flat NER layers until no outer entities are extracted. Extensive evaluation shows that our dynamic model outperforms state-of-the-art feature-based systems on nested NER, achieving 74.7{\%} and 72.2{\%} on GENIA and ACE2005 datasets, respectively, in terms of F-score.
Multimodal Frame Identification with Multilingual Evaluation
Authors: Teresa Botschen, Iryna Gurevych, Jan-Christoph Klie, Hatem Mousselly-Sergieh, Stefan Roth
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Common Sense Reasoning, Semantic Role Labeling, Word Embeddings

An essential step in FrameNet Semantic Role Labeling is the Frame Identification (FrameId) task, which aims at disambiguating a situation around a predicate. Whilst current FrameId methods rely on textual representations only, we hypothesize that FrameId can profit from a richer understanding of the situational context. Such contextual information can be obtained from common sense knowledge, which is more present in images than in text. In this paper, we extend a state-of-the-art FrameId system in order to effectively leverage multimodal representations. We conduct a comprehensive evaluation on the English FrameNet and its German counterpart SALSA. Our analysis shows that for the German data, textual representations are still competitive with multimodal ones. However on the English data, our multimodal FrameId approach outperforms its unimodal counterpart, setting a new state of the art. Its benefits are particularly apparent in dealing with ambiguous and rare instances, the main source of errors of current systems. For research purposes, we release (a) the implementation of our system, (b) our evaluation splits for SALSA 2.0, and (c) the embeddings for synsets and IMAGINED words.
Learning to Collaborate for Question Answering and Asking
Authors: Duyu Tang, Nan Duan, Zhao Yan, Zhirui Zhang, Yibo Sun, Shujie Liu, Yuanhua Lv, Ming Zhou
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Answer Selection, Generative Adversarial Network, Question Answering, Question Generation, Question-Generation

Question answering (QA) and question generation (QG) are closely related tasks that could improve each other; however, the connection of these two tasks is not well explored in literature. In this paper, we give a systematic study that seeks to leverage the connection to improve both QA and QG. We present a training algorithm that generalizes both Generative Adversarial Network (GAN) and Generative Domain-Adaptive Nets (GDAN) under the question answering scenario. The two key ideas are improving the QG model with QA through incorporating additional QA-specific signal as the loss function, and improving the QA model with QG through adding artificially generated training instances. We conduct experiments on both document based and knowledge based question answering tasks. We have two main findings. Firstly, the performance of a QG model (e.g in terms of BLEU score) could be easily improved by a QA model via policy gradient. Secondly, directly applying GAN that regards all the generated questions as negative instances could not improve the accuracy of the QA model. Learning when to regard generated questions as positive instances could bring performance boost.
Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation
Authors: Mingmin Jin, Xin Luo, Huiling Zhu, Hankz Hankui Zhuo
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Clustering, Collaborative Filtering, Recommendation Systems

With the rise of e-commerce, people are accustomed to writing their reviews after receiving the goods. These comments are so important that a bad review can have a direct impact on others buying. Besides, the abundant information within user reviews is very useful for extracting user preferences and item properties. In this paper, we investigate the approach to effectively utilize review information for recommender systems. The proposed model is named LSTM-Topic matrix factorization (LTMF) which integrates both LSTM and Topic Modeling for review understanding. In the experiments on popular review dataset Amazon , our LTMF model outperforms previous proposed HFT model and ConvMF model in rating prediction. Furthermore, LTMF shows the better ability on making topic clustering than traditional topic model based method, which implies integrating the information from deep learning and topic modeling is a meaningful approach to make a better understanding of reviews.
Deconfounded Lexicon Induction for Interpretable Social Science
Authors: Reid Pryzant, Kelly Shen, Dan Jurafsky, Stefan Wagner
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6

NLP algorithms are increasingly used in computational social science to take linguistic observations and predict outcomes like human preferences or actions. Making these social models transparent and interpretable often requires identifying features in the input that predict outcomes while also controlling for potential confounds. We formalize this need as a new task: inducing a lexicon that is predictive of a set of target variables yet uncorrelated to a set of confounding variables. We introduce two deep learning algorithms for the task. The first uses a bifurcated architecture to separate the explanatory power of the text and confounds. The second uses an adversarial discriminator to force confound-invariant text encodings. Both elicit lexicons from learned weights and attentional scores. We use them to induce lexicons that are predictive of timely responses to consumer complaints (controlling for product), enrollment from course descriptions (controlling for subject), and sales from product descriptions (controlling for seller). In each domain our algorithms pick words that are associated with \textit{narrative persuasion}; more predictive and less confound-related than those of standard feature weighting and lexicon induction techniques like regression and log odds.
Detecting Denial-of-Service Attacks from Social Media Text: Applying NLP to Computer Security
Authors: Nathanael Chambers, Ben Fry, James McMasters
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Computer Security

This paper describes a novel application of NLP models to detect denial of service attacks using only social media as evidence. Individual networks are often slow in reporting attacks, so a detection system from public data could better assist a response to a broad attack across multiple services. We explore NLP methods to use social media as an indirect measure of network service status. We describe two learning frameworks for this task: a feed-forward neural network and a partially labeled LDA model. Both models outperform previous work by significant margins (20{\%} F1 score). We further show that the topic-based model enables the first fine-grained analysis of how the public reacts to ongoing network attacks, discovering multiple {``}stages{''} of observation. This is the first model that both detects network attacks (with best performance) and provides an analysis of when and how the public interprets service outages. We describe the models, present experiments on the largest twitter DDoS corpus to date, and conclude with an analysis of public reactions based on the learned model{'}s output.