In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: Consider Input: We consider the problem of accurately recovering a matrix B of size M M , which represents a probability distribution overM outcomes, given access to an observed matrix of "counts" generated by taking independent samples from the distribution B. How can structural properties of the underlying matrix B be leveraged to yield computationally efficient and information theoretically optimal reconstruction algorithms? When can accurate reconstruction be accomplished in the sparse data regime? This basic problem lies at the core of a number of questions that are currently being considered by different communities, including community detection in sparse random graphs, learning structured models such as topic models or hidden Markov models, and the efforts from the natural language processing community to compute "word embeddings". Many aspects of this problemboth in terms of learning and property testing/estimation and on both the algorithmic and information theoretic sidesremain open. Our results apply to the setting where B has a particular rank 2 structure. For this setting, we propose an efficient (and practically viable) algorithm that accurately recovers the underlying M M matrix using (M) samples. This result easily translates to (M) sample algorithms for learning topic models with two topics over dictionaries of size M , and learning hidden Markov Models with two hidden states and observation distributions supported on M elements. These linear sample complexities are optimal, up to constant factors, in an extremely strong sense: even testing basic properties of the underlying matrix (such as whether it has rank 1 or 2) requires (M) samples. Furthermore, we provide an even stronger lower bound where distinguishing whether a sequence of observations were drawn from the uniform distribution over M observations versus being generated by an HMM with two hidden states requires (M) observations. This precludes sublinear-sample hypothesis tests for basic properties, such as identity or uniformity, as well as sublinear sample estimators for quantities such as the entropy rate of HMMs. This impossibility of sublinear-sample property testing in these settings is intriguing and underscores the significant differences between these structured settings and the standard setting of drawing i.i.d samples from an unstructured distribution of support size M . MIT. Email: qqh@mit.edu. University of Washington. Email: sham@cs.washington.edu Stanford University. Email: kweihao@gmail.com Stanford University. Email: valiant@stanford.edu. Gregory and Weihao's contributions were supported by NSF CAREER Award CCF-1351108, and a research grant from the Okawa Foundation. ar X iv :1 60 2. 06 58 6v 1 [ cs .L G ] 2 1 Fe b 20 16

Output: Recovering Structured Probability Matrices

Input: Consider Input: The field of speech recognition is in the midst of a paradigm shift: end-to-end neural networks are challenging the dominance of hidden Markov models as a core technology. Using an attention mechanism in a recurrent encoder-decoder architecture solves the dynamic time alignment problem, allowing joint end-to-end training of the acoustic and language modeling components. In this paper we extend the end-to-end framework to encompass microphone array signal processing for noise suppression and speech enhancement within the acoustic encoding network. This allows the beamforming components to be optimized jointly within the recognition architecture to improve the end-to-end speech recognition objective. Experiments on the noisy speech benchmarks (CHiME-4 and AMI) show that our multichannel end-to-end system outperformed the attention-based baseline with input from a conventional adaptive beamformer.

Output: Multichannel End-to-end Speech Recognition

Input: Consider Input: We present an ensemble approach for categorizing search query entities in the recruitment domain. Understanding the types of entities expressed in a search query (Company, Skill, Job Title, etc.) enables more intelligent information retrieval based upon those entities compared to a traditional keyword-based search. Because search queries are typically very short, leveraging a traditional bag-of-words model to identify entity types would be inappropriate due to the lack of contextual information. Our approach instead combines clues from different sources of varying complexity in order to collect real-world knowledge about query entities. We employ distributional semantic representations of query entities through two models: 1) contextual vectors generated from encyclopedic corpora like Wikipedia, and 2) high dimensional word embedding vectors generated from millions of job postings using word2vec. Additionally, our approach utilizes both entity linguistic properties obtained from WordNet and ontological properties extracted from DBpedia. We evaluate our approach on a data set created at CareerBuilder; the largest job board in the US. The data set contains entities extracted from millions of job seekers/recruiters search queries, job postings, and resume documents. After constructing the distributional vectors of search entities, we use supervised machine learning to infer search entity types. Empirical results show that our approach outperforms the state-of-the-art word2vec distributional semantics model trained on Wikipedia. Moreover, we achieve microaveraged F1 score of 97% using the proposed distributional representations ensemble.
Output: Entity Type Recognition using an Ensemble of Distributional Semantic Models to Enhance Query Understanding