ymoslem/acl-abstracts huggingface dataset:
Knowledge-Enriched Two-Layered Attention Network for Sentiment Analysis (2018)

We propose a novel two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis. The novel two-layered attention network takes advantage of the external knowledge bases to improve the sentiment prediction. It uses the Knowledge Graph Embedding generated using the WordNet. We build our model by combining the two-layered attention network with the supervised model based on Support Vector Regression using a Multilayer Perceptron network for sentiment analysis. We evaluate our model on the benchmark dataset of SemEval 2017 Task 5. Experimental results show that the proposed model surpasses the top system of SemEval 2017 Task 5. The model performs significantly better by improving the state-of-the-art system at SemEval 2017 Task 5 by 1.7 and 3.7 points for sub-tracks 1 and 2 respectively.
Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books (2018)

Books have the power to make us feel happiness, sadness, pain, surprise, or sorrow. An author`s dexterity in the use of these emotions captivates readers and makes it difficult for them to put the book down. In this paper, we model the flow of emotions over a book using recurrent neural networks and quantify its usefulness in predicting success in books. We obtained the best weighted F1-score of 69{\%} for predicting books' success in a multitask setting (simultaneously predicting success and genre of books).
Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis (2018)

Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. Present neural-based models exploit aspect and its contextual information in the sentence but largely ignore the inter-aspect dependencies. In this paper, we incorporate this pattern by simultaneous classification of all aspects in a sentence along with temporal dependency processing of their corresponding sentence representations using recurrent networks. Results on the benchmark SemEval 2014 dataset suggest the effectiveness of our proposed approach.
Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment (2018)

In recent past, social media has emerged as an active platform in the context of healthcare and medicine. In this paper, we present a study where medical user`s opinions on health-related issues are analyzed to capture the medical sentiment at a blog level. The medical sentiments can be studied in various facets such as medical condition, treatment, and medication that characterize the overall health status of the user. Considering these facets, we treat analysis of this information as a multi-task classification problem. In this paper, we adopt a novel adversarial learning approach for our multi-task learning framework to learn the sentiment`s strengths expressed in a medical blog. Our evaluation shows promising results for our target tasks.
Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis (2018)

While neural networks have been shown to achieve impressive results for sentence-level sentiment analysis, targeted aspect-based sentiment analysis (TABSA) {---} extraction of fine-grained opinion polarity w.r.t. a pre-defined set of aspects {---} remains a difficult task. Motivated by recent advances in memory-augmented models for machine reading, we propose a novel architecture, utilising external {\textquotedblleft}memory chains{\textquotedblright} with a delayed memory update mechanism to track entities. On a TABSA task, the proposed model demonstrates substantial improvements over state-of-the-art approaches, including those using external knowledge bases.
Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation (2018)

We combine two of the most popular approaches to automated Grammatical Error Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC based on Neural Machine Translation (NMT). The hybrid system achieves new state-of-the-art results on the CoNLL-2014 and JFLEG benchmarks. This GEC system preserves the accuracy of SMT output and, at the same time, generates more fluent sentences as it typical for NMT. Our analysis shows that the created systems are closer to reaching human-level performance than any other GEC system reported so far.
Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks (2018)

We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of a single fact. Adopting a straightforward decomposition of the problem into entity detection, entity linking, relation prediction, and evidence combination, we explore simple yet strong baselines. On the popular SimpleQuestions dataset, we find that basic LSTMs and GRUs plus a few heuristics yield accuracies that approach the state of the art, and techniques that do not use neural networks also perform reasonably well. These results show that gains from sophisticated deep learning techniques proposed in the literature are quite modest and that some previous models exhibit unnecessary complexity.
Looking for Structure in Lexical and Acoustic-Prosodic Entrainment Behaviors (2018)

Entrainment has been shown to occur for various linguistic features individually. Motivated by cognitive theories regarding linguistic entrainment, we analyze speakers' overall entrainment behaviors and search for an underlying structure. We consider various measures of both acoustic-prosodic and lexical entrainment, measuring the latter with a novel application of two previously introduced methods in addition to a standard high-frequency word measure. We present a negative result of our search, finding no meaningful correlations, clusters, or principal components in various entrainment measures, and discuss practical and theoretical implications.
Modeling Semantic Plausibility by Injecting World Knowledge (2018)

Distributional data tells us that a man can swallow candy, but not that a man can swallow a paintball, since this is never attested. However both are physically plausible events. This paper introduces the task of semantic plausibility: recognizing plausible but possibly novel events. We present a new crowdsourced dataset of semantic plausibility judgments of single events such as man swallow paintball. Simple models based on distributional representations perform poorly on this task, despite doing well on selection preference, but injecting manually elicited knowledge about entity properties provides a substantial performance boost. Our error analysis shows that our new dataset is a great testbed for semantic plausibility models: more sophisticated knowledge representation and propagation could address many of the remaining errors.
A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling (2018)

Intent detection and slot filling are two main tasks for building a spoken language understanding(SLU) system. Multiple deep learning based models have demonstrated good results on these tasks . The most effective algorithms are based on the structures of sequence to sequence models (or {\textquotedblleft}encoder-decoder{\textquotedblright} models), and generate the intents and semantic tags either using separate models. Most of the previous studies, however, either treat the intent detection and slot filling as two separate parallel tasks, or use a sequence to sequence model to generate both semantic tags and intent. None of the approaches consider the cross-impact between the intent detection task and the slot filling task. In this paper, new Bi-model based RNN semantic frame parsing network structures are designed to perform the intent detection and slot filling tasks jointly, by considering their cross-impact to each other using two correlated bidirectional LSTMs (BLSTM). Our Bi-model structure with a decoder achieves state-of-art result on the benchmark ATIS data, with about 0.5{\%} intent accuracy improvement and 0.9 {\%} slot filling improvement.
A Comparison of Two Paraphrase Models for Taxonomy Augmentation (2018)

Taxonomies are often used to look up the concepts they contain in text documents (for instance, to classify a document). The more comprehensive the taxonomy, the higher recall the application has that uses the taxonomy. In this paper, we explore automatic taxonomy augmentation with paraphrases. We compare two state-of-the-art paraphrase models based on Moses, a statistical Machine Translation system, and a sequence-to-sequence neural network, trained on a paraphrase datasets with respect to their abilities to add novel nodes to an existing taxonomy from the risk domain. We conduct component-based and task-based evaluations. Our results show that paraphrasing is a viable method to enrich a taxonomy with more terms, and that Moses consistently outperforms the sequence-to-sequence neural model. To the best of our knowledge, this is the first approach to augment taxonomies with paraphrases.
A Laypeople Study on Terminology Identification across Domains and Task Definitions (2018)

This paper introduces a new dataset of term annotation. Given that even experts vary significantly in their understanding of termhood, and that term identification is mostly performed as a binary task, we offer a novel perspective to explore the common, natural understanding of what constitutes a term: Laypeople annotate single-word and multi-word terms, across four domains and across four task definitions. Analyses based on inter-annotator agreement offer insights into differences in term specificity, term granularity and subtermhood.
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network (2018)

In this paper, we propose a novel embedding model, named ConvKB, for knowledge base completion. Our model ConvKB advances state-of-the-art models by employing a convolutional neural network, so that it can capture global relationships and transitional characteristics between entities and relations in knowledge bases. In ConvKB, each triple (head entity, relation, tail entity) is represented as a 3-column matrix where each column vector represents a triple element. This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps. These feature maps are then concatenated into a single feature vector representing the input triple. The feature vector is multiplied with a weight vector via a dot product to return a score. This score is then used to predict whether the triple is valid or not. Experiments show that ConvKB achieves better link prediction performance than previous state-of-the-art embedding models on two benchmark datasets WN18RR and FB15k-237.
Cross-language Article Linking Using Cross-Encyclopedia Entity Embedding (2018)

Cross-language article linking (CLAL) is the task of finding corresponding article pairs of different languages across encyclopedias. This task is a difficult disambiguation problem in which one article must be selected among several candidate articles with similar titles and contents. Existing works focus on engineering text-based or link-based features for this task, which is a time-consuming job, and some of these features are only applicable within the same encyclopedia. In this paper, we address these problems by proposing cross-encyclopedia entity embedding. Unlike other works, our proposed method does not rely on known cross-language pairs. We apply our method to CLAL between English Wikipedia and Chinese Baidu Baike. Our features improve performance relative to the baseline by 29.62{\%}. Tested 30 times, our system achieved an average improvement of 2.76{\%} over the current best system (26.86{\%} over baseline), a statistically significant result.
Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations (2018)

Identifying the most dominant and central event of a document, which governs and connects other foreground and background events in the document, is useful for many applications, such as text summarization, storyline generation and text segmentation. We observed that the central event of a document usually has many coreferential event mentions that are scattered throughout the document for enabling a smooth transition of subtopics. Our empirical experiments, using gold event coreference relations, have shown that the central event of a document can be well identified by mining properties of event coreference chains. But the performance drops when switching to system predicted event coreference relations. In addition, we found that the central event can be more accurately identified by further considering the number of sub-events as well as the realis status of an event.
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding (2018)

Entity recognition is a widely benchmarked task in natural language processing due to its massive applications. The state-of-the-art solution applies a neural architecture named BiLSTM-CRF to model the language sequences. In this paper, we propose an entity recognition system that improves this neural architecture with two novel techniques. The first technique is Multi-Task Data Selection, which ensures the consistency of data distribution and labeling guidelines between source and target datasets. The other one is constrained decoding using knowledge base. The decoder of the model operates at the document level, and leverages global and external information sources to further improve performance. Extensive experiments have been conducted to show the advantages of each technique. Our system achieves state-of-the-art results on the English entity recognition task in KBP 2017 official evaluation, and it also yields very strong results in other languages.
Keep Your Bearings: Lightly-Supervised Information Extraction with Ladder Networks That Avoids Semantic Drift (2018)

We propose a novel approach to semi-supervised learning for information extraction that uses ladder networks (Rasmus et al., 2015). In particular, we focus on the task of named entity classification, defined as identifying the correct label (e.g., person or organization name) of an entity mention in a given context. Our approach is simple, efficient and has the benefit of being robust to semantic drift, a dominant problem in most semi-supervised learning systems. We empirically demonstrate the superior performance of our system compared to the state-of-the-art on two standard datasets for named entity classification. We obtain between 62{\%} and 200{\%} improvement over the state-of-art baseline on these two datasets.
Semi-Supervised Event Extraction with Paraphrase Clusters (2018)

Supervised event extraction systems are limited in their accuracy due to the lack of available training data. We present a method for self-training event extraction systems by bootstrapping additional training data. This is done by taking advantage of the occurrence of multiple mentions of the same event instances across newswire articles from multiple sources. If our system can make a high-confidence extraction of some mentions in such a cluster, it can then acquire diverse training examples by adding the other mentions as well. Our experiments show significant performance improvements on multiple event extractors over ACE 2005 and TAC-KBP 2015 datasets.
Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text (2018)

Relation classification is an important semantic processing task in the field of natural language processing. In this paper, we propose the task of relation classification for Chinese literature text. A new dataset of Chinese literature text is constructed to facilitate the study in this task. We present a novel model, named Structure Regularized Bidirectional Recurrent Convolutional Neural Network (SR-BRCNN), to identify the relation between entities. The proposed model learns relation representations along the shortest dependency path (SDP) extracted from the structure regularized dependency tree, which has the benefits of reducing the complexity of the whole model. Experimental results show that the proposed method significantly improves the F1 score by 10.3, and outperforms the state-of-the-art approaches on Chinese literature text.
Syntactic Patterns Improve Information Extraction for Medical Search (2018)

Medical professionals search the published literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s) of interest. In this paper we demonstrate how features encoding syntactic patterns improve the performance of state-of-the-art sequence tagging models (both neural and linear) for information extraction of these medically relevant categories. We present an analysis of the type of patterns exploited and of the semantic space induced for these, i.e., the distributed representations learned for identified multi-token patterns. We show that these learned representations differ substantially from those of the constituent unigrams, suggesting that the patterns capture contextual information that is otherwise lost.
Syntactically Aware Neural Architectures for Definition Extraction (2018)

Automatically identifying definitional knowledge in text corpora (Definition Extraction or DE) is an important task with direct applications in, among others, Automatic Glossary Generation, Taxonomy Learning, Question Answering and Semantic Search. It is generally cast as a binary classification problem between definitional and non-definitional sentences. In this paper we present a set of neural architectures combining Convolutional and Recurrent Neural Networks, which are further enriched by incorporating linguistic information via syntactic dependencies. Our experimental results in the task of sentence classification, on two benchmarking DE datasets (one generic, one domain-specific), show that these models obtain consistent state of the art results. Furthermore, we demonstrate that models trained on clean Wikipedia-like definitions can successfully be applied to more noisy domain-specific corpora.
A Dynamic Oracle for Linear-Time 2-Planar Dependency Parsing (2018)

We propose an efficient dynamic oracle for training the 2-Planar transition-based parser, a linear-time parser with over 99{\%} coverage on non-projective syntactic corpora. This novel approach outperforms the static training strategy in the vast majority of languages tested and scored better on most datasets than the arc-hybrid parser enhanced with the Swap transition, which can handle unrestricted non-projectivity.
Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics? (2018)

We evaluate the performance of state-of-the-art algorithms for automatic cognate detection by comparing how useful automatically inferred cognates are for the task of phylogenetic inference compared to classical manually annotated cognate sets. Our findings suggest that phylogenies inferred from automated cognate sets come close to phylogenies inferred from expert-annotated ones, although on average, the latter are still superior. We conclude that future work on phylogenetic reconstruction can profit much from automatic cognate detection. Especially where scholars are merely interested in exploring the bigger picture of a language family`s phylogeny, algorithms for automatic cognate detection are a useful complement for current research on language phylogenies.
Automatically Selecting the Best Dependency Annotation Design with Dynamic Oracles (2018)

This work introduces a new strategy to compare the numerous conventions that have been proposed over the years for expressing dependency structures and discover the one for which a parser will achieve the highest parsing performance. Instead of associating each sentence in the training set with a single gold reference we propose to consider a set of references encoding alternative syntactic representations. Training a parser with a dynamic oracle will then automatically select among all alternatives the reference that will be predicted with the highest accuracy. Experiments on the UD corpora show the validity of this approach.
Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning (2018)

In formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain their logical formulas. Here, it is important that the parser processes the sentences consistently; failing to recognize the similar syntactic structure results in inconsistent predicate argument structures among them, in which case the succeeding theorem proving is doomed to failure. In this work, we present a simple method to extend an existing CCG parser to parse a set of sentences consistently, which is achieved with an inter-sentence modeling with Markov Random Fields (MRF). When combined with existing logic-based systems, our method always shows improvement in the RTE experiments on English and Japanese languages.
Exploiting Dynamic Oracles to Train Projective Dependency Parsers on Non-Projective Trees (2018)

Because the most common transition systems are projective, training a transition-based dependency parser often implies to either ignore or rewrite the non-projective training examples, which has an adverse impact on accuracy. In this work, we propose a simple modification of dynamic oracles, which enables the use of non-projective data when training projective parsers. Evaluation on 73 treebanks shows that our method achieves significant gains (+2 to +7 UAS for the most non-projective languages) and consistently outperforms traditional projectivization and pseudo-projectivization approaches.
Improving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers (2018)

We generalize Cohen, G{\'o}mez-Rodr{\'i}guez, and Satta`s (2011) parser to a family of non-projective transition-based dependency parsers allowing polynomial-time exact inference. This includes novel parsers with better coverage than Cohen et al. (2011), and even a variant that reduces time complexity to $O(n^6)$, improving over the known bounds in exact inference for non-projective transition-based parsing. We hope that this piece of theoretical work inspires design of novel transition systems with better coverage and better run-time guarantees.
Towards a Variability Measure for Multiword Expressions (2018)

One of the most outstanding properties of multiword expressions (MWEs), especially verbal ones (VMWEs), important both in theoretical models and applications, is their idiosyncratic variability. Some MWEs are always continuous, while some others admit certain types of insertions. Components of some MWEs are rarely or never modified, while some others admit either specific or unrestricted modification. This unpredictable variability profile of MWEs hinders modeling and processing them as {\textquotedblleft}words-with-spaces{\textquotedblright} on the one hand, and as regular syntactic structures on the other hand. Since variability of MWEs is a matter of scale rather than a binary property, we propose a 2-dimensional language-independent measure of variability dedicated to verbal MWEs based on syntactic and discontinuity-related clues. We assess its relevance with respect to a linguistic benchmark and its utility for the tasks of VMWE classification and variant identification on a French corpus.