mtybilly/Instruct2DS huggingface dataset:
Joint Learning for Emotion Classification and Emotion Cause Detection
Authors: Wenjun Hou, Xiyao Cheng, Shoushan Li Ying Chen
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis. Considering that emotion classification and emotion cause detection need different kinds of features (affective and event-based separately), we propose a joint encoder which uses a unified framework to extract features for both sub-tasks and a joint model trainer which simultaneously learns two models for the two sub-tasks separately. Our experiments on Chinese microblogs show that the joint approach is very promising.
Exploring Optimism and Pessimism in Twitter Using Deep Learning
Authors: Liviu P. Dinu, Bogdan Dumitru Cornelia Caragea
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need such support, and for minimizing the negative influence among users and maximizing the spread of positive attitudes and ideas. In this paper, we explore a range of deep learning models to predict optimism and pessimism in Twitter at both tweet and user level and show that these models substantially outperform traditional machine learning classifiers used in prior work. In addition, we show evidence that a sentiment classifier would not be sufficient for accurately predicting optimism and pessimism in Twitter. Last, we study the verb tense usage as well as the presence of polarity words in optimistic and pessimistic tweets.
Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning
Authors: Daniel Hardt, Dirk Hovy Sotiris Lamprinidis
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Newspapers need to attract readers with headlines, anticipating their readers' preferences. These preferences rely on topical, structural, and lexical factors. We model each of these factors in a multi-task GRU network to predict headline popularity. We find that pre-trained word embeddings provide significant improvements over untrained embeddings, as do the combination of two auxiliary tasks, news-section prediction and part-of-speech tagging. However, we also find that performance is very similar to that of a simple Logistic Regression model over character n-grams. Feature analysis reveals structural patterns of headline popularity, including the use of forward-looking deictic expressions and second person pronouns.
Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates
Authors: Jiachen Du, Lidong Bing, Ruifeng Xu Di Chen
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Inferring the agreement/disagreement relation in debates, especially in online debates, is one of the fundamental tasks in argumentation mining. The expressions of agreement/disagreement usually rely on argumentative expressions in text as well as interactions between participants in debates. Previous works usually lack the capability of jointly modeling these two factors. To alleviate this problem, this paper proposes a hybrid neural attention model which combines self and cross attention mechanism to locate salient part from textual context and interaction between users. Experimental results on three (dis)agreement inference datasets show that our model outperforms the state-of-the-art models.
Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information
Authors: Tommaso Fornaciari Dirk Hovy
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Most text-classification approaches represent the input based on textual features, either feature-based or continuous. However, this ignores strong non-linguistic similarities like homophily: people within a demographic group use language more similar to each other than to non-group members. We use homophily cues to retrofit text-based author representations with non-linguistic information, and introduce a trade-off parameter. This approach increases in-class similarity between authors, and improves classification performance by making classes more linearly separable. We evaluate the effect of our method on two author-attribute prediction tasks with various training-set sizes and parameter settings. We find that our method can significantly improve classification performance, especially when the number of labels is large and limited labeled data is available. It is potentially applicable as preprocessing step to any text-classification task.
A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation
Authors: Xiao Sun Jingyuan Li
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Traditional neural language models tend to generate generic replies with poor logic and no emotion. In this paper, a syntactically constrained bidirectional-asynchronous approach for emotional conversation generation (E-SCBA) is proposed to address this issue. In our model, pre-generated emotion keywords and topic keywords are asynchronously introduced into the process of decoding. It is much different from most existing methods which generate replies from the first word to the last. Through experiments, the results indicate that our approach not only improves the diversity of replies, but gains a boost on both logic and emotion compared with baselines.
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning
Authors: Qi Chen, Lei Sha, Sujian Li, Xu Sun, Houfeng Wang, Lintao Zhang Chen Shi
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: The lack of labeled data is one of the main challenges when building a task-oriented dialogue system. Existing dialogue datasets usually rely on human labeling, which is expensive, limited in size, and in low coverage. In this paper, we instead propose our framework auto-dialabel to automatically cluster the dialogue intents and slots. In this framework, we collect a set of context features, leverage an autoencoder for feature assembly, and adapt a dynamic hierarchical clustering method for intent and slot labeling. Experimental results show that our framework can promote human labeling cost to a great extent, achieve good intent clustering accuracy (84.1%), and provide reasonable and instructive slot labeling results.
Extending Neural Generative Conversational Model using External Knowledge Sources
Authors: Joelle Pineau Prasanna Parthasarathi
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: The use of connectionist approaches in conversational agents has been progressing rapidly due to the availability of large corpora. However current generative dialogue models often lack coherence and are content poor. This work proposes an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models. We focus on Sequence-to-Sequence (Seq2Seq) conversational agents trained with the Reddit News dataset, and consider incorporating external knowledge from Wikipedia summaries as well as from the NELL knowledge base. Our experiments show faster training time and improved perplexity when leveraging external knowledge.
Modeling Temporality of Human Intentions by Domain Adaptation
Authors: Lixing Liu, Kate Carey, Joshua Woolley, Stefan Scherer, Brian Borsari Xiaolei Huang
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Categorizing patient`s intentions in conversational assessment can help decision making in clinical treatments. Many conversation corpora span broaden a series of time stages. However, it is not clear that how the themes shift in the conversation impact on the performance of human intention categorization (eg., patients might show different behaviors during the beginning versus the end). This paper proposes a method that models the temporal factor by using domain adaptation on clinical dialogue corpora, Motivational Interviewing (MI). We deploy Bi-LSTM and topic model jointly to learn language usage change across different time sessions. We conduct experiments on the MI corpora to show the promising improvement after considering temporality in the classification task.
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
Authors: Jingjing Xu, Junyang Lin, Qi Zeng, Xu Sun Liangchen Luo
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Generating semantically coherent responses is still a major challenge in dialogue generation. Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation between the whole meanings of inputs and outputs. To address this problem, we propose an Auto-Encoder Matching (AEM) model to learn such dependency. The model contains two auto-encoders and one mapping module. The auto-encoders learn the semantic representations of inputs and responses, and the mapping module learns to connect the utterance-level representations. Experimental results from automatic and human evaluations demonstrate that our model is capable of generating responses of high coherence and fluency compared to baseline models.
A Dataset for Document Grounded Conversations
Authors: Shrimai Prabhumoye, Alan W Black Kangyan Zhou
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: This paper introduces a document grounded dataset for conversations. We define “Document Grounded Conversations” as conversations that are about the contents of a specified document. In this dataset the specified documents were Wikipedia articles about popular movies. The dataset contains 4112 conversations with an average of 21.43 turns per conversation. This positions this dataset to not only provide a relevant chat history while generating responses but also provide a source of information that the models could use. We describe two neural architectures that provide benchmark performance on the task of generating the next response. We also evaluate our models for engagement and fluency, and find that the information from the document helps in generating more engaging and fluent responses.
Out-of-domain Detection based on Generative Adversarial Network
Authors: Sangjun Koo, Hwanjo Yu, Gary Geunbae Lee Seonghan Ryu
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: The main goal of this paper is to develop out-of-domain (OOD) detection for dialog systems. We propose to use only in-domain (IND) sentences to build a generative adversarial network (GAN) of which the discriminator generates low scores for OOD sentences. To improve basic GANs, we apply feature matching loss in the discriminator, use domain-category analysis as an additional task in the discriminator, and remove the biases in the generator. Thereby, we reduce the huge effort of collecting OOD sentences for training OOD detection. For evaluation, we experimented OOD detection on a multi-domain dialog system. The experimental results showed the proposed method was most accurate compared to the existing methods.
Listening Comprehension over Argumentative Content
Authors: Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim Shachar Mirkin
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: This paper presents a task for machine listening comprehension in the argumentation domain and a corresponding dataset in English. We recorded 200 spontaneous speeches arguing for or against 50 controversial topics. For each speech, we formulated a question, aimed at confirming or rejecting the occurrence of potential arguments in the speech. Labels were collected by listening to the speech and marking which arguments were mentioned by the speaker. We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset. All data used in this work is freely available for research.
Using active learning to expand training data for implicit discourse relation recognition
Authors: Yu Hong, Huibin Ruan, Jianmin Yao, Min Zhang, Guodong Zhou Yang Xu
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We tackle discourse-level relation recognition, a problem of determining semantic relations between text spans. Implicit relation recognition is challenging due to the lack of explicit relational clues. The increasingly popular neural network techniques have been proven effective for semantic encoding, whereby widely employed to boost semantic relation discrimination. However, learning to predict semantic relations at a deep level heavily relies on a great deal of training data, but the scale of the publicly available data in this field is limited. In this paper, we follow Rutherford and Xue (2015) to expand the training data set using the corpus of explicitly-related arguments, by arbitrarily dropping the overtly presented discourse connectives. On the basis, we carry out an experiment of sampling, in which a simple active learning approach is used, so as to take the informative instances for data expansion. The goal is to verify whether the selective use of external data not only reduces the time consumption of retraining but also ensures a better system performance. Using the expanded training data, we retrain a convolutional neural network (CNN) based classifer which is a simplified version of Qin et al. (2016)`s stacking gated relation recognizer. Experimental results show that expanding the training set with small-scale carefully-selected external data yields substantial performance gain, with the improvements of about 4% for accuracy and 3.6% for F-score. This allows a weak classifier to achieve a comparable performance against the state-of-the-art systems.
Learning To Split and Rephrase From Wikipedia Edit History
Authors: Manaal Faruqui, John Alex, Jason Baldridge, Dipanjan Das Jan A. Botha
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning. We extract a rich new dataset for this task by mining Wikipedia`s edit history: WikiSplit contains one million naturally occurring sentence rewrites, providing sixty times more distinct split examples and a ninety times larger vocabulary than the WebSplit corpus introduced by Narayan et al. (2017) as a benchmark for this task. Incorporating WikiSplit as training data produces a model with qualitatively better predictions that score 32 BLEU points above the prior best result on the WebSplit benchmark.
BLEU is Not Suitable for the Evaluation of Text Simplification
Authors: Omri Abend, Ari Rappoport Elior Sulem
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS). TS includes both lexical and structural aspects. In this paper we show that BLEU is not suitable for the evaluation of sentence splitting, the major structural simplification operation. We manually compiled a sentence splitting gold standard corpus containing multiple structural paraphrases, and performed a correlation analysis with human judgments. We find low or no correlation between BLEU and the grammaticality and meaning preservation parameters where sentence splitting is involved. Moreover, BLEU often negatively correlates with simplicity, essentially penalizing simpler sentences.
S2SPMN: A Simple and Effective Framework for Response Generation with Relevant Information
Authors: Chenliang Li Jiaxin Pei
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: How to generate relevant and informative responses is one of the core topics in response generation area. Following the task formulation of machine translation, previous works mainly consider response generation task as a mapping from a source sentence to a target sentence. To realize this mapping, existing works tend to design intuitive but complex models. However, the relevant information existed in large dialogue corpus is mainly overlooked. In this paper, we propose Sequence to Sequence with Prototype Memory Network (S2SPMN) to exploit the relevant information provided by the large dialogue corpus to enhance response generation. Specifically, we devise two simple approaches in S2SPMN to select the relevant information (named prototypes) from the dialogue corpus. These prototypes are then saved into prototype memory network (PMN). Furthermore, a hierarchical attention mechanism is devised to extract the semantic information from the PMN to assist the response generation process. Empirical studies reveal the advantage of our model over several classical and strong baselines.
Improving Reinforcement Learning Based Image Captioning with Natural Language Prior
Authors: Shiyu Chang, Mo Yu, Kun Bai Tszhang Guo
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Recently, Reinforcement Learning (RL) approaches have demonstrated advanced performance in image captioning by directly optimizing the metric used for testing. However, this shaped reward introduces learning biases, which reduces the readability of generated text. In addition, the large sample space makes training unstable and slow. To alleviate these issues, we propose a simple coherent solution that constrains the action space using an n-gram language prior. Quantitative and qualitative evaluations on benchmarks show that RL with the simple add-on module performs favorably against its counterpart in terms of both readability and speed of convergence. Human evaluation results show that our model is more human readable and graceful. The implementation will become publicly available upon the acceptance of the paper.
Training for Diversity in Image Paragraph Captioning
Authors: Alexander Rush, George Han Luke Melas-Kyriazi
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Image paragraph captioning models aim to produce detailed descriptions of a source image. These models use similar techniques as standard image captioning models, but they have encountered issues in text generation, notably a lack of diversity between sentences, that have limited their effectiveness. In this work, we consider applying sequence-level training for this task. We find that standard self-critical training produces poor results, but when combined with an integrated penalty on trigram repetition produces much more diverse paragraphs. This simple training approach improves on the best result on the Visual Genome paragraph captioning dataset from 16.9 to 30.6 CIDEr, with gains on METEOR and BLEU as well, without requiring any architectural changes.
A Graph-theoretic Summary Evaluation for ROUGE
Authors: Mohammad Ebrahimi, Raymond Wong, Fang Chen Elaheh ShafieiBavani
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: ROUGE is one of the first and most widely used evaluation metrics for text summarization. However, its assessment merely relies on surface similarities between peer and model summaries. Consequently, ROUGE is unable to fairly evaluate summaries including lexical variations and paraphrasing. We propose a graph-based approach adopted into ROUGE to evaluate summaries based on both lexical and semantic similarities. Experiment results over TAC AESOP datasets show that exploiting the lexico-semantic similarity of the words used in summaries would significantly help ROUGE correlate better with human judgments.
Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation
Authors: Andreas Vlachos Hardy Hardy
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Recent work on abstractive summarization has made progress with neural encoder-decoder architectures. However, such models are often challenged due to their lack of explicit semantic modeling of the source document and its summary. In this paper, we extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which we guide using the source document. We demonstrate that this guidance improves summarization results by 7.4 and 10.5 points in ROUGE-2 using gold standard AMR parses and parses obtained from an off-the-shelf parser respectively. We also find that the summarization performance on later parses is 2 ROUGE-2 points higher than that of a well-established neural encoder-decoder approach trained on a larger dataset.
Evaluating Multiple System Summary Lengths: A Case Study
Authors: David Gabay, Hadar Ronen, Judit Bar-Ilan, Yael Amsterdamer, Ani Nenkova, Ido Dagan Ori Shapira
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Practical summarization systems are expected to produce summaries of varying lengths, per user needs. While a couple of early summarization benchmarks tested systems across multiple summary lengths, this practice was mostly abandoned due to the assumed cost of producing reference summaries of multiple lengths. In this paper, we raise the research question of whether reference summaries of a single length can be used to reliably evaluate system summaries of multiple lengths. For that, we have analyzed a couple of datasets as a case study, using several variants of the ROUGE metric that are standard in summarization evaluation. Our findings indicate that the evaluation protocol in question is indeed competitive. This result paves the way to practically evaluating varying-length summaries with simple, possibly existing, summarization benchmarks.
Neural Latent Extractive Document Summarization
Authors: Mirella Lapata, Furu Wei, Ming Zhou Xingxing Zhang
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Extractive summarization models need sentence level labels, which are usually created with rule-based methods since most summarization datasets only have document summary pairs. These labels might be suboptimal. We propose a latent variable extractive model, where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries. During training, the loss can come directly from gold summaries. Experiments on CNN/Dailymail dataset show our latent extractive model outperforms a strong extractive baseline trained on rule-based labels and also performs competitively with several recent models.
On the Abstractiveness of Neural Document Summarization
Authors: Jin-ge Yao, Rui Yan Fangfang Zhang
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Many modern neural document summarization systems based on encoder-decoder networks are designed to produce abstractive summaries. We attempted to verify the degree of abstractiveness of modern neural abstractive summarization systems by calculating overlaps in terms of various types of units. Upon the observation that many abstractive systems tend to be near-extractive in practice, we also implemented a pure copy system, which achieved comparable results as abstractive summarizers while being far more computationally efficient. These findings suggest the possibility for future efforts towards more efficient systems that could better utilize the vocabulary in the original document.
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning
Authors: Zhongyu Wei, Yaqian Zhou, Xuanjing Huang Yucheng Wang
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Automatic essay scoring (AES) is the task of assigning grades to essays without human interference. Existing systems for AES are typically trained to predict the score of each single essay at a time without considering the rating schema. In order to address this issue, we propose a reinforcement learning framework for essay scoring that incorporates quadratic weighted kappa as guidance to optimize the scoring system. Experiment results on benchmark datasets show the effectiveness of our framework.
Identifying Well-formed Natural Language Questions
Authors: Dipanjan Das Manaal Faruqui
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. Here, we introduce a new task of identifying a well-formed natural language question. We construct and release a dataset of 25,100 publicly available questions classified into well-formed and non-wellformed categories and report an accuracy of 70.7% on the test set. We also show that our classifier can be used to improve the performance of neural sequence-to-sequence models for generating questions for reading comprehension.
Self-Governing Neural Networks for On-Device Short Text Classification
Authors: Zornitsa Kozareva Sujith Ravi
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications. Yet, one of the biggest challenges is running these complex networks on devices such as mobile phones or smart watches with tiny memory footprint and low computational capacity. We propose on-device Self-Governing Neural Networks (SGNNs), which learn compact projection vectors with local sensitive hashing. The key advantage of SGNNs over existing work is that they surmount the need for pre-trained word embeddings and complex networks with huge parameters. We conduct extensive evaluation on dialog act classification and show significant improvement over state-of-the-art results. Our findings show that SGNNs are effective at capturing low-dimensional semantic text representations, while maintaining high accuracy.
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization
Authors: Jiyi Li, Fumiyo Fukumoto Kazuya Shimura
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We focus on the multi-label categorization task for short texts and explore the use of a hierarchical structure (HS) of categories. In contrast to the existing work using non-hierarchical flat model, the method leverages the hierarchical relations between the pre-defined categories to tackle the data sparsity problem. The lower the HS level, the less the categorization performance. Because the number of training data per category in a lower level is much smaller than that in an upper level. We propose an approach which can effectively utilize the data in the upper levels to contribute the categorization in the lower levels by applying the Convolutional Neural Network (CNN) with a fine-tuning technique. The results using two benchmark datasets show that proposed method, Hierarchical Fine-Tuning based CNN (HFT-CNN) is competitive with the state-of-the-art CNN based methods.
A Hierarchical Neural Attention-based Text Classifier
Authors: Yue Dong, Jackie Chi Kit Cheung, Derek Ruths Koustuv Sinha
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Deep neural networks have been displaying superior performance over traditional supervised classifiers in text classification. They learn to extract useful features automatically when sufficient amount of data is presented. However, along with the growth in the number of documents comes the increase in the number of categories, which often results in poor performance of the multiclass classifiers. In this work, we use external knowledge in the form of topic category taxonomies to aide the classification by introducing a deep hierarchical neural attention-based classifier. Our model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability.
Labeled Anchors and a Scalable, Transparent, and Interactive Classifier
Authors: Stephen Cowley, Wilson Fearn, Emily Hales, Kevin Seppi Jeffrey Lund
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We propose Labeled Anchors, an interactive and supervised topic model based on the anchor words algorithm (Arora et al., 2013). Labeled Anchors is similar to Supervised Anchors (Nguyen et al., 2014) in that it extends the vector-space representation of words to include document labels. However, our formulation also admits a classifier which requires no training beyond inferring topics, which means our approach is also fast enough to be interactive. We run a small user study that demonstrates that untrained users can interactively update topics in order to improve classification accuracy.
Coherence-Aware Neural Topic Modeling
Authors: Ramesh Nallapati, Bing Xiang Ran Ding
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Topic models are evaluated based on their ability to describe documents well (i.e. low perplexity) and to produce topics that carry coherent semantic meaning. In topic modeling so far, perplexity is a direct optimization target. However, topic coherence, owing to its challenging computation, is not optimized for and is only evaluated after training. In this work, under a neural variational inference framework, we propose methods to incorporate a topic coherence objective into the training process. We demonstrate that such a coherence-aware topic model exhibits a similar level of perplexity as baseline models but achieves substantially higher topic coherence.
Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models
Authors: Nasser Zalmout, Nizar Habash Daniel Watson
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Text normalization is an important enabling technology for several NLP tasks. Recently, neural-network-based approaches have outperformed well-established models in this task. However, in languages other than English, there has been little exploration in this direction. Both the scarcity of annotated data and the complexity of the language increase the difficulty of the problem. To address these challenges, we use a sequence-to-sequence model with character-based attention, which in addition to its self-learned character embeddings, uses word embeddings pre-trained with an approach that also models subword information. This provides the neural model with access to more linguistic information especially suitable for text normalization, without large parallel corpora. We show that providing the model with word-level features bridges the gap for the neural network approach to achieve a state-of-the-art F1 score on a standard Arabic language correction shared task dataset.
Topic Intrusion for Automatic Topic Model Evaluation
Authors: Jey Han Lau, Timothy Baldwin Shraey Bhatia
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Topic coherence is increasingly being used to evaluate topic models and filter topics for end-user applications. Topic coherence measures how well topic words relate to each other, but offers little insight on the utility of the topics in describing the documents. In this paper, we explore the topic intrusion task --- the task of guessing an outlier topic given a document and a few topics --- and propose a method to automate it. We improve upon the state-of-the-art substantially, demonstrating its viability as an alternative method for topic model evaluation.
Supervised and Unsupervised Methods for Robust Separation of Section Titles and Prose Text in Web Documents
Authors: Shomir Wilson, Norman Sadeh Abhijith Athreya Mysore Gopinath
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: The text in many web documents is organized into a hierarchy of section titles and corresponding prose content, a structure which provides potentially exploitable information on discourse structure and topicality. However, this organization is generally discarded during text collection, and collecting it is not straightforward: the same visual organization can be implemented in a myriad of different ways in the underlying HTML. To remedy this, we present a flexible system for automatically extracting the hierarchical section titles and prose organization of web documents irrespective of differences in HTML representation. This system uses features from syntax, semantics, discourse and markup to build two models which classify HTML text into section titles and prose text. When tested on three different domains of web text, our domain-independent system achieves an overall precision of 0.82 and a recall of 0.98. The domain-dependent variation produces very high precision (0.99) at the expense of recall (0.75). These results exhibit a robust level of accuracy suitable for enhancing question answering, information extraction, and summarization.
SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation
Authors: Hieu Pham, Zihang Dai, Graham Neubig Xinyi Wang
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: In this work, we examine methods for data augmentation for text-based tasks such as neural machine translation (NMT). We formulate the design of a data augmentation policy with desirable properties as an optimization problem, and derive a generic analytic solution. This solution not only subsumes some existing augmentation schemes, but also leads to an extremely simple data augmentation strategy for NMT: randomly replacing words in both the source sentence and the target sentence with other random words from their corresponding vocabularies. We name this method SwitchOut. Experiments on three translation datasets of different scales show that SwitchOut yields consistent improvements of about 0.5 BLEU, achieving better or comparable performances to strong alternatives such as word dropout (Sennrich et al., 2016a). Code to implement this method is included in the appendix.
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder
Authors: Jiahui Geng, Hermann Ney Yunsu Kim
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we propose simple yet effective methods to improve word-by-word translation of cross-lingual embeddings, using only monolingual corpora but without any back-translation. We integrate a language model for context-aware search, and use a novel denoising autoencoder to handle reordering. Our system surpasses state-of-the-art unsupervised translation systems without costly iterative training. We also analyze the effect of vocabulary size and denoising type on the translation performance, which provides better understanding of learning the cross-lingual word embedding and its usage in translation.
Decipherment of Substitution Ciphers with Neural Language Models
Authors: Anahita Mansouri Bigvand, Anoop Sarkar Nishant Kambhatla
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Decipherment of homophonic substitution ciphers using language models is a well-studied task in NLP. Previous work in this topic scores short local spans of possible plaintext decipherments using n-gram language models. The most widely used technique is the use of beam search with n-gram language models proposed by Nuhn et al.(2013). We propose a beam search algorithm that scores the entire candidate plaintext at each step of the decipherment using a neural language model. We augment beam search with a novel rest cost estimation that exploits the prediction power of a neural language model. We compare against the state of the art n-gram based methods on many different decipherment tasks. On challenging ciphers such as the Beale cipher we provide significantly better error rates with much smaller beam sizes.
Rapid Adaptation of Neural Machine Translation to New Languages
Authors: Junjie Hu Graham Neubig
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible. We propose methods based on starting with massively multilingual “seed models”, which can be trained ahead-of-time, and then continuing training on data related to the LRL. We contrast a number of strategies, leading to a novel, simple, yet effective method of “similar-language regularization”, where we jointly train on both a LRL of interest and a similar high-resourced language to prevent over-fitting to small LRL data. Experiments demonstrate that massively multilingual models, even without any explicit adaptation, are surprisingly effective, achieving BLEU scores of up to 15.5 with no data from the LRL, and that the proposed similar-language regularization method improves over other adaptation methods by 1.7 BLEU points average over 4 LRL settings.
Compact Personalized Models for Neural Machine Translation
Authors: Patrick Simianer, John DeNero Joern Wuebker
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture--combining a state-of-the-art self-attentive model with compact domain adaptation--provides high quality personalized machine translation that is both space and time efficient.
Supervised Domain Enablement Attention for Personalized Domain Classification
Authors: Young-Bum Kim Joo-Kyung Kim
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: In large-scale domain classification for natural language understanding, leveraging each user`s domain enablement information, which refers to the preferred or authenticated domains by the user, with attention mechanism has been shown to improve the overall domain classification performance. In this paper, we propose a supervised enablement attention mechanism, which utilizes sigmoid activation for the attention weighting so that the attention can be computed with more expressive power without the weight sum constraint of softmax attention. The attention weights are explicitly encouraged to be similar to the corresponding elements of the output one-hot vector, and self-distillation is used to leverage the attention information of the other enabled domains. By evaluating on the actual utterances from a large-scale IPDA, we show that our approach significantly improves domain classification performance
A Deep Neural Network Sentence Level Classification Method with Context Information
Authors: Johann Petrak, Angus Roberts Xingyi Song
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification. This context is, however, often ignored. Where methods do make use of context, only small amounts are considered, making it difficult to scale. We present a new method for sentence classification, Context-LSTM-CNN, that makes use of potentially large contexts. The method also utilizes long-range dependencies within the sentence being classified, using an LSTM, and short-span features, using a stacked CNN. Our experiments demonstrate that this approach consistently improves over previous methods on two different datasets.
Towards Dynamic Computation Graphs via Sparse Latent Structure
Authors: André F. T. Martins, Claire Cardie Vlad Niculae
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Deep NLP models benefit from underlying structures in the data---e.g., parse trees---typically extracted using off-the-shelf parsers. Recent attempts to jointly learn the latent structure encounter a tradeoff: either make factorization assumptions that limit expressiveness, or sacrifice end-to-end differentiability. Using the recently proposed SparseMAP inference, which retrieves a sparse distribution over latent structures, we propose a novel approach for end-to-end learning of latent structure predictors jointly with a downstream predictor. To the best of our knowledge, our method is the first to enable unrestricted dynamic computation graph construction from the global latent structure, while maintaining differentiability.
Convolutional Neural Networks with Recurrent Neural Filters
Authors: Yi Yang
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: We introduce a class of convolutional neural networks (CNNs) that utilize recurrent neural networks (RNNs) as convolution filters. A convolution filter is typically implemented as a linear affine transformation followed by a non-linear function, which fails to account for language compositionality. As a result, it limits the use of high-order filters that are often warranted for natural language processing tasks. In this work, we model convolution filters with RNNs that naturally capture compositionality and long-term dependencies in language. We show that simple CNN architectures equipped with recurrent neural filters (RNFs) achieve results that are on par with the best published ones on the Stanford Sentiment Treebank and two answer sentence selection datasets.
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model
Authors: Lingfei Wu, Zhiguo Wang, Mo Yu, Liwei Chen, Vadim Sheinin Kun Xu
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency or constituent trees. In this paper, we first propose to use the syntactic graph to represent three types of syntactic information, i.e., word order, dependency and constituency features; then employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.
Retrieval-Based Neural Code Generation
Authors: Raphael Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, Graham Neubig Shirley Anugrah Hayati
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: In models to generate program source code from natural language, representing this code in a tree structure has been a common approach. However, existing methods often fail to generate complex code correctly due to a lack of ability to memorize large and complex structures. We introduce RECODE, a method based on subtree retrieval that makes it possible to explicitly reference existing code examples within a neural code generation model. First, we retrieve sentences that are similar to input sentences using a dynamic-programming-based sentence similarity scoring method. Next, we extract n-grams of action sequences that build the associated abstract syntax tree. Finally, we increase the probability of actions that cause the retrieved n-gram action subtree to be in the predicted code. We show that our approach improves the performance on two code generation tasks by up to +2.6 BLEU.
SQL-to-Text Generation with Graph-to-Sequence Model
Authors: Lingfei Wu, Zhiguo Wang, Yansong Feng, Vadim Sheinin Kun Xu
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2018

Abstract: Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. In this paper, we propose a graph-to-sequence model to encode the global structure information into node embeddings. This model can effectively learn the correlation between the SQL query pattern and its interpretation. Experimental results on the WikiSQL dataset and Stackoverflow dataset show that our model outperforms the Seq2Seq and Tree2Seq baselines, achieving the state-of-the-art performance.