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.
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Question: This paper presents an empirical study of two widely-used sequence prediction models, Conditional Random Fields (CRFs) and Long Short-Term Memory Networks (LSTMs), on two fundamental tasks for Vietnamese text processing, including part-of-speech tagging and named entity recognition. We show that a strong lower bound for labeling accuracy can be obtained by relying only on simple word-based features with minimal handcrafted feature engineering, of 90.65% and 86.03% performance scores on the standard test sets for the two tasks respectively. In particular, we demonstrate empirically the surprising efficiency of word embeddings in both of the two tasks, with both of the two models. We point out that the state-of-the-art LSTMs model does not always outperform significantly the traditional CRFs model, especially on moderate-sized data sets. Finally, we give some suggestions and discussions for efficient use of sequence labeling models in practical applications.

Answer: An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing


Question: In natural-language discourse, related events tend to appear near each other to describe a larger scenario. Such structures can be formalized by the notion of a frame (a.k.a. template), which comprises a set of related events and prototypical participants and event transitions. Identifying frames is a prerequisite for information extraction and natural language generation, and is usually done manually. Methods for inducing frames have been proposed recently, but they typically use ad hoc procedures and are difficult to diagnose or extend. In this paper, we propose the first probabilistic approach to frame induction, which incorporates frames, events, participants as latent topics and learns those frame and event transitions that best explain the text. The number of frames is inferred by a novel application of a split-merge method from syntactic parsing. In end-to-end evaluations from text to induced frames and extracted facts, our method produced state-of-the-art results while substantially reducing engineering effort.

Answer: Probabilistic Frame Induction∗


Question: Recent advances have made it feasible to apply the stochastic variational paradigm to a collapsed representation of latent Dirichlet allocation (LDA). While the stochastic variational paradigm has successfully been applied to an uncollapsed representation of the hierarchical Dirichlet process (HDP), no attempts to apply this type of inference in a collapsed setting of non-parametric topic modeling have been put forward so far. In this paper we explore such a collapsed stochastic variational Bayes inference for the HDP. The proposed online algorithm is easy to implement and accounts for the inference of hyper-parameters. First experiments show a promising improvement in predictive performance. 1 Background We begin by considering a model where each document d is a mixture θd of K discrete topicdistributions φk over a vocabulary of V terms. Let zdi ∈ {1, ..,K} denote the topic of the i word wdi ∈ {1, .., V } in document d ∈ {1, .., D} and place Dirichlet priors on the parameters θd, φk. We have zdi | θd ∼ Discrete(θd) , θd ∼ Dirichlet(απ) , wdi | zdi, {φk} ∼ Discrete(φzdi) , φk ∼ Dirichlet(β) , where π is the top-level distribution over topics, and α and β are concentration parameters. While the dimensionality of K is fixed in latent Dirichlet allocation (LDA), we want the model to determine the number of topics needed. Consequently we follow the assumptions made by the hierarchical Dirichlet process (HDP) [1] of a countable but infinite number of topics, of which only a finite number is used in the posterior. Our prior π is constructed by a truncated sick-breaking process [2],

Answer:
Practical Collapsed Stochastic Variational Inference for the HDP