Exploiting multi-word units in history-based probabilistic generation

We present a simple history-based model for sentence generation from LFG f-structures, which improves on the accuracy of previous models by breaking down PCFG independence assumptions so that more f-structure conditioning context is used in the prediction of grammar rule expansions. In addition, we present work on experiments with named entities and other multi-word units, showing a statistically significant improvement of generation accuracy. Tested on section 23 of the PennWall Street Journal Treebank, the techniques described in this paper improve BLEU scores from 66.52 to 68.82, and coverage from 98.18% to 99.96%

Exploiting multi-word units in statistical parsing and generation

Syntactic parsing is an important prerequisite for many natural language processing (NLP) applications. The task refers to the process of generating the tree of syntactic nodes with associated phrase category labels corresponding to a sentence. Our objective is to improve upon statistical models for syntactic parsing by leveraging multi-word units (MWUs) such as named entities and other classes of multi-word expressions. Multi-word units are phrases that are lexically, syntactically and/or semantically idiosyncratic in that they are to at least some degree non-compositional. If such units are identified prior to, or as part of, the parsing process their boundaries can be exploited as islands of certainty within the very large (and often highly ambiguous) search space. Luckily, certain types of MWUs can be readily identified in an automatic fashion (using a variety of techniques) to a near-human level of accuracy. We carry out a number of experiments which integrate knowledge about different classes of MWUs in several commonly deployed parsing architectures. In a supplementary set of experiments, we attempt to exploit these units in the converse operation to statistical parsing---statistical generation (in our case, surface realisation from Lexical-Functional Grammar f-structures). We show that, by exploiting knowledge about MWUs, certain classes of parsing and generation decisions are more accurately resolved. This translates to improvements in overall parsing and generation results which, although modest, are demonstrably significant

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

The Microsoft 2017 Conversational Speech Recognition System

We describe the 2017 version of Microsoft's conversational speech recognition system, in which we update our 2016 system with recent developments in neural-network-based acoustic and language modeling to further advance the state of the art on the Switchboard speech recognition task. The system adds a CNN-BLSTM acoustic model to the set of model architectures we combined previously, and includes character-based and dialog session aware LSTM language models in rescoring. For system combination we adopt a two-stage approach, whereby subsets of acoustic models are first combined at the senone/frame level, followed by a word-level voting via confusion networks. We also added a confusion network rescoring step after system combination. The resulting system yields a 5.1\% word error rate on the 2000 Switchboard evaluation set

Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time

Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.Comment: In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018