pwc-archive/papers-with-abstracts huggingface dataset:
Spotting Spurious Data with Neural Networks
Authors: Hadi Amiri, Timothy Miller, Guergana Savova
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6

Automatic identification of spurious instances (those with potentially wrong labels in datasets) can improve the quality of existing language resources, especially when annotations are obtained through crowdsourcing or automatically generated based on coded rankings. In this paper, we present effective approaches inspired by queueing theory and psychology of learning to automatically identify spurious instances in datasets. Our approaches discriminate instances based on their {``}difficulty to learn,{''} determined by a downstream learner. Our methods can be applied to any dataset assuming the existence of a neural network model for the target task of the dataset. Our best approach outperforms competing state-of-the-art baselines and has a MAP of 0.85 and 0.22 in identifying spurious instances in synthetic and carefully-crowdsourced real-world datasets respectively.
The Timing of Lexical Memory Retrievals in Language Production
Authors: Jeremy Cole, David Reitter
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Retrieval

This paper explores the time course of lexical memory retrieval by modeling fluent language production. The duration of retrievals is predicted using the ACT-R cognitive architecture. In a large-scale observational study of a spoken corpus, we find that language production at a time point preceding a word is sped up or slowed down depending on activation of that word. This computational analysis has consequences for the theoretical model of language production. The results point to interference between lexical and phonological stages as well as a quantifiable buffer for lexical information that opens up the possibility of non-sequential retrievals.
Unsupervised Induction of Linguistic Categories with Records of Reading, Speaking, and Writing
Authors: Maria Barrett, Ana Valeria Gonz{\'a}lez-Gardu{\~n}o, Lea Frermann, Anders S{\o}gaard
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Chunking, POS, TAG, Word Embeddings

When learning POS taggers and syntactic chunkers for low-resource languages, different resources may be available, and often all we have is a small tag dictionary, motivating type-constrained unsupervised induction. Even small dictionaries can improve the performance of unsupervised induction algorithms. This paper shows that performance can be further improved by including data that is readily available or can be easily obtained for most languages, i.e., eye-tracking, speech, or keystroke logs (or any combination thereof). We project information from all these data sources into shared spaces, in which the union of words is represented. For English unsupervised POS induction, the additional information, which is not required at test time, leads to an average error reduction on Ontonotes domains of 1.5{\%} over systems augmented with state-of-the-art word embeddings. On Penn Treebank the best model achieves 5.4{\%} error reduction over a word embeddings baseline. We also achieve significant improvements for syntactic chunk induction. Our analysis shows that improvements are even bigger when the available tag dictionaries are smaller.
Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog
Authors: Kaixin Ma, Tomasz Jurczyk, Jinho D. Choi
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Question Answering, Reading Comprehension

This paper presents a new corpus and a robust deep learning architecture for a task in reading comprehension, passage completion, on multiparty dialog. Given a dialog in text and a passage containing factual descriptions about the dialog where mentions of the characters are replaced by blanks, the task is to fill the blanks with the most appropriate character names that reflect the contexts in the dialog. Since there is no dataset that challenges the task of passage completion in this genre, we create a corpus by selecting transcripts from a TV show that comprise 1,681 dialogs, generating passages for each dialog through crowdsourcing, and annotating mentions of characters in both the dialog and the passages. Given this dataset, we build a deep neural model that integrates rich feature extraction from convolutional neural networks into sequence modeling in recurrent neural networks, optimized by utterance and dialog level attentions. Our model outperforms the previous state-of-the-art model on this task in a different genre using bidirectional LSTM, showing a 13.0+{\%} improvement for longer dialogs. Our analysis shows the effectiveness of the attention mechanisms and suggests a direction to machine comprehension on multiparty dialog.
Dialog Generation Using Multi-Turn Reasoning Neural Networks
Authors: Xianchao Wu, Ander Mart{\'\i}nez, Momo Klyen
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Constituency Parsing, Image Captioning, Learning-To-Rank, Machine Translation, Sentence, Speech Recognition, Text Generation

In this paper, we propose a generalizable dialog generation approach that adapts multi-turn reasoning, one recent advancement in the field of document comprehension, to generate responses ({``}answers{''}) by taking current conversation session context as a {``}document{''} and current query as a {``}question{''}. The major idea is to represent a conversation session into memories upon which attention-based memory reading mechanism can be performed multiple times, so that (1) user{'}s query is properly extended by contextual clues and (2) optimal responses are step-by-step generated. Considering that the speakers of one conversation are not limited to be one, we separate the single memory used for document comprehension into different groups for speaker-specific topic and opinion embedding. Namely, we utilize the queries{'} memory, the responses{'} memory, and their unified memory, following the time sequence of the conversation session. Experiments on Japanese 10-sentence (5-round) conversation modeling show impressive results on how multi-turn reasoning can produce more diverse and acceptable responses than state-of-the-art single-turn and non-reasoning baselines.
LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics
Authors: Zhen Xu, Nan Jiang, Bingquan Liu, Wenge Rong, Bowen Wu, Baoxun Wang, Zhuoran Wang, Xiaolong Wang
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Diversity, Machine Translation, Response Generation

It has been proven that automatic conversational agents can be built up using the Endto-End Neural Response Generation (NRG) framework, and such a data-driven methodology requires a large number of dialog pairs for model training and reasonable evaluation metrics for testing. This paper proposes a Large Scale Domain-Specific Conversational Corpus (LSDSCC) composed of high-quality queryresponse pairs extracted from the domainspecific online forum, with thorough preprocessing and cleansing procedures. Also, a testing set, including multiple diverse responses annotated for each query, is constructed, and on this basis, the metrics for measuring the diversity of generated results are further presented. We evaluate the performances of neural dialog models with the widely applied diversity boosting strategies on the proposed dataset. The experimental results have shown that our proposed corpus can be taken as a new benchmark dataset for the NRG task, and the presented metrics are promising to guide the optimization of NRG models by quantifying the diversity of the generated responses reasonably.
EMR Coding with Semi-Parametric Multi-Head Matching Networks
Authors: Anthony Rios, Ramakanth Kavuluru
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Few-Shot Learning, General Classification, Multi-Label Classification, Multi-Label Text Classification, text-classification, Text Classification

Coding EMRs with diagnosis and procedure codes is an indispensable task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and misinterpretation of a patient{'}s well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility. In this paper, we present a new neural network architecture that combines ideas from few-shot learning matching networks, multi-label loss functions, and convolutional neural networks for text classification to significantly outperform other state-of-the-art models. Our evaluations are conducted using a well known de-identified EMR dataset (MIMIC) with a variety of multi-label performance measures.
Mining Evidences for Concept Stock Recommendation
Authors: Qi Liu, Yue Zhang
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Deep Reinforcement Learning, Information Retrieval, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval

We investigate the task of mining relevant stocks given a topic of concern on emerging capital markets, for which there is lack of structural understanding. Deep learning is leveraged to mine evidences from large scale textual data, which contain valuable market information. In particular, distributed word similarities trained over large scale raw texts are taken as a basis of relevance measuring, and deep reinforcement learning is leveraged to learn a strategy of topic expansion, given a small amount of manually labeled data from financial analysts. Results on two Chinese stock market datasets show that our method outperforms a strong baseline using information retrieval techniques.
Binarized LSTM Language Model
Authors: Xuan Liu, Di Cao, Kai Yu
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, model, speech-recognition, Speech Recognition

Long short-term memory (LSTM) language model (LM) has been widely investigated for automatic speech recognition (ASR) and natural language processing (NLP). Although excellent performance is obtained for large vocabulary tasks, tremendous memory consumption prohibits the use of LSTM LM in low-resource devices. The memory consumption mainly comes from the word embedding layer. In this paper, a novel binarized LSTM LM is proposed to address the problem. Words are encoded into binary vectors and other LSTM parameters are further binarized to achieve high memory compression. This is the first effort to investigate binary LSTM for large vocabulary LM. Experiments on both English and Chinese LM and ASR tasks showed that can achieve a compression ratio of 11.3 without any loss of LM and ASR performances and a compression ratio of 31.6 with acceptable minor performance degradation.
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos
Authors: Devamanyu Hazarika, Soujanya Poria, Amir Zadeh, Erik Cambria, Louis-Philippe Morency, Roger Zimmermann
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Emotion Recognition, Emotion Recognition in Conversation

Emotion recognition in conversations is crucial for the development of empathetic machines. Present methods mostly ignore the role of inter-speaker dependency relations while classifying emotions in conversations. In this paper, we address recognizing utterance-level emotions in dyadic conversational videos. We propose a deep neural framework, termed Conversational Memory Network (CMN), which leverages contextual information from the conversation history. In particular, CMN uses multimodal approach comprising audio, visual and textual features with gated recurrent units to model past utterances of each speaker into memories. These memories are then merged using attention-based hops to capture inter-speaker dependencies. Experiments show a significant improvement of 3 âˆ’ 4{\%} in accuracy over the state of the art.
How Time Matters: Learning Time-Decay Attention for Contextual Spoken Language Understanding in Dialogues
Authors: Shang-Yu Su, Pei-Chieh Yuan, Yun-Nung Chen
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Dialogue State Tracking, Image Captioning, Machine Translation, Sentence, Slot Filling, Spoken Dialogue Systems, Spoken Language Understanding

Spoken language understanding (SLU) is an essential component in conversational systems. Most SLU components treats each utterance independently, and then the following components aggregate the multi-turn information in the separate phases. In order to avoid error propagation and effectively utilize contexts, prior work leveraged history for contextual SLU. However, most previous models only paid attention to the related content in history utterances, ignoring their temporal information. In the dialogues, it is intuitive that the most recent utterances are more important than the least recent ones, in other words, time-aware attention should be in a decaying manner. Therefore, this paper designs and investigates various types of time-decay attention on the sentence-level and speaker-level, and further proposes a flexible universal time-decay attention mechanism. The experiments on the benchmark Dialogue State Tracking Challenge (DSTC4) dataset show that the proposed time-decay attention mechanisms significantly improve the state-of-the-art model for contextual understanding performance.
Towards Understanding Text Factors in Oral Reading
Authors: Anastassia Loukina, Van Rynald T. Liceralde, Beata Beigman Klebanov
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Language Acquisition

Using a case study, we show that variation in oral reading rate across passages for professional narrators is consistent across readers and much of it can be explained using features of the texts being read. While text complexity is a poor predictor of the reading rate, a substantial share of variability can be explained by timing and story-based factors with performance reaching r=0.75 for unseen passages and narrator.
Stacking with Auxiliary Features for Visual Question Answering
Authors: Nazneen Fatema Rajani, Raymond Mooney
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Common Sense Reasoning, Question Answering, Visual Question Answering, Visual Question Answering (VQA), Word Embeddings

Visual Question Answering (VQA) is a well-known and challenging task that requires systems to jointly reason about natural language and vision. Deep learning models in various forms have been the standard for solving VQA. However, some of these VQA models are better at certain types of image-question pairs than other models. Ensembling VQA models intelligently to leverage their diverse expertise is, therefore, advantageous. Stacking With Auxiliary Features (SWAF) is an intelligent ensembling technique which learns to combine the results of multiple models using features of the current problem as context. We propose four categories of auxiliary features for ensembling for VQA. Three out of the four categories of features can be inferred from an image-question pair and do not require querying the component models. The fourth category of auxiliary features uses model-specific explanations. In this paper, we describe how we use these various categories of auxiliary features to improve performance for VQA. Using SWAF to effectively ensemble three recent systems, we obtain a new state-of-the-art. Our work also highlights the advantages of explainable AI models.
Neural Text Generation in Stories Using Entity Representations as Context
Authors: Elizabeth Clark, Yangfeng Ji, Noah A. Smith
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Dialogue Generation, Representation Learning, Sentence, Text Generation

We introduce an approach to neural text generation that explicitly represents entities mentioned in the text. Entity representations are vectors that are updated as the text proceeds; they are designed specifically for narrative text like fiction or news stories. Our experiments demonstrate that modeling entities offers a benefit in two automatic evaluations: mention generation (in which a model chooses which entity to mention next and which words to use in the mention) and selection between a correct next sentence and a distractor from later in the same story. We also conduct a human evaluation on automatically generated text in story contexts; this study supports our emphasis on entities and suggests directions for further research.
Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset
Authors: Pavithra Rajendran, Danushka Bollegala, Simon Parsons
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Abstract Argumentation, Argument Mining, General Classification, Opinion Mining, Sentiment Analysis

Online reviews have become a popular portal among customers making decisions about purchasing products. A number of corpora of reviews have been widely investigated in NLP in general, and, in particular, in argument mining. This is a subset of NLP that deals with extracting arguments and the relations among them from user-based content. A major problem faced by argument mining research is the lack of human-annotated data. In this paper, we investigate the use of weakly supervised and semi-supervised methods for automatically annotating data, and thus providing large annotated datasets. We do this by building on previous work that explores the classification of opinions present in reviews based whether the stance is expressed explicitly or implicitly. In the work described here, we automatically annotate stance as implicit or explicit and our results show that the datasets we generate, although noisy, can be used to learn better models for implicit/explicit opinion classification.
Automatic Dialogue Generation with Expressed Emotions
Authors: Chenyang Huang, Osmar Za{\"\i}ane, Amine Trabelsi, Nouha Dziri
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Dialogue Generation

Despite myriad efforts in the literature designing neural dialogue generation systems in recent years, very few consider putting restrictions on the response itself. They learn from collections of past responses and generate one based on a given utterance without considering, speech act, desired style or emotion to be expressed. In this research, we address the problem of forcing the dialogue generation to express emotion. We present three models that either concatenate the desired emotion with the source input during the learning, or push the emotion in the decoder. The results, evaluated with an emotion tagger, are encouraging with all three models, but present better outcome and promise with our model that adds the emotion vector in the decoder.
Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network
Authors: Chenliang Li, Weiran Xu, Si Li, Sheng Gao
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Abstractive Text Summarization, Decoder, Text Summarization

Neural network models, based on the attentional encoder-decoder model, have good capability in abstractive text summarization. However, these models are hard to be controlled in the process of generation, which leads to a lack of key information. We propose a guiding generation model that combines the extractive method and the abstractive method. Firstly, we obtain keywords from the text by a extractive model. Then, we introduce a Key Information Guide Network (KIGN), which encodes the keywords to the key information representation, to guide the process of generation. In addition, we use a prediction-guide mechanism, which can obtain the long-term value for future decoding, to further guide the summary generation. We evaluate our model on the CNN/Daily Mail dataset. The experimental results show that our model leads to significant improvements.
Neural Poetry Translation
Authors: Marjan Ghazvininejad, Yejin Choi, Kevin Knight
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, Rhythm, Translation

We present the first neural poetry translation system. Unlike previous works that often fail to produce any translation for fixed rhyme and rhythm patterns, our system always translates a source text to an English poem. Human evaluation of the translations ranks the quality as acceptable 78.2{\%} of the time.
A Corpus of Non-Native Written English Annotated for Metaphor
Authors: Beata Beigman Klebanov, Chee Wee (Ben) Leong, Michael Flor
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6

We present a corpus of 240 argumentative essays written by non-native speakers of English annotated for metaphor. The corpus is made publicly available. We provide benchmark performance of state-of-the-art systems on this new corpus, and explore the relationship between writing proficiency and metaphor use.
Humor Recognition Using Deep Learning
Authors: Peng-Yu Chen, Von-Wun Soo
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Deep Learning

Humor is an essential but most fascinating element in personal communication. How to build computational models to discover the structures of humor, recognize humor and even generate humor remains a challenge and there have been yet few attempts on it. In this paper, we construct and collect four datasets with distinct joke types in both English and Chinese and conduct learning experiments on humor recognition. We implement a Convolutional Neural Network (CNN) with extensive filter size, number and Highway Networks to increase the depth of networks. Results show that our model outperforms in recognition of different types of humor with benchmarks collected in both English and Chinese languages on accuracy, precision, and recall in comparison to previous works.
Training Structured Prediction Energy Networks with Indirect Supervision
Authors: Amirmohammad Rooshenas, Aishwarya Kamath, Andrew McCallum
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Prediction, Semantic Segmentation, Structured Prediction

This paper introduces rank-based training of structured prediction energy networks (SPENs). Our method samples from output structures using gradient descent and minimizes the ranking violation of the sampled structures with respect to a scalar scoring function defined with domain knowledge. We have successfully trained SPEN for citation field extraction without any labeled data instances, where the only source of supervision is a simple human-written scoring function. Such scoring functions are often easy to provide; the SPEN then furnishes an efficient structured prediction inference procedure.
Si O No, Que Penses? Catalonian Independence and Linguistic Identity on Social Media
Authors: Ian Stewart, Yuval Pinter, Jacob Eisenstein
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6

Political identity is often manifested in language variation, but the relationship between the two is still relatively unexplored from a quantitative perspective. This study examines the use of Catalan, a language local to the semi-autonomous region of Catalonia in Spain, on Twitter in discourse related to the 2017 independence referendum. We corroborate prior findings that pro-independence tweets are more likely to include the local language than anti-independence tweets. We also find that Catalan is used more often in referendum-related discourse than in other contexts, contrary to prior findings on language variation. This suggests a strong role for the Catalan language in the expression of Catalonian political identity.