Multi-objective problems with correlated objectives are a class of problems that deserve specific attention. In contrast to typical multi-objective problems, they do not require the identification of trade-offs between the objectives, as (near-) optimal solutions for any objective are (near-) optimal for every objective. Intelligently combining the feedback from these objectives, instead of only looking at a single one, can improve optimization. This class of problems is very relevant in reinforcement learning, as any single-objective reinforcement learning problem can be framed as such a multi-objective problem using multiple reward shaping functions. After discussing this problem class, we propose a solution technique for such reinforcement learning problems, called adaptive objective selection. This technique makes a temporal difference learner estimate the Q-function for each objective in parallel, and introduces a way of measuring confidence in these estimates. This confidence metric is then used to choose which objective's estimates to use for action selection. We show significant improvements in performance over other plausible techniques on two problem domains. Finally, we provide an intuitive analysis of the technique's decisions, yielding insights into the nature of the problems being solved.

Most active learning methods avoid model selection by training models of one type (SVMs, boosted trees, etc.) using one pre-defined set of model hyperparameters. We propose an algorithm that actively samples data to simultaneously train a set of candidate models (different model types and/or different hyperparameters) and also select the best model from this set. The algorithm actively samples points for training that are most likely to improve the accuracy of the more promising candidate models, and also samples points for model selection---all samples count against the same labeling budget. This exposes a natural trade-off between the focused active sampling that is most effective for training models, and the unbiased sampling that is better for model selection. We empirically demonstrate on six test problems that this algorithm is nearly as effective as an active learning oracle that knows the optimal model in advance.

The conventional statistical machine translation (SMT) models, such as phrase-based models (Koehn et al. 2007), formal syntax-based models (Chiang 2007; Xiong, Liu, and Aiming at retaining the semantic meaning during the Lin 2006) and linguistically syntax-based models (Liu, Liu, translation process, we propose a Recursive Neural Network and Lin 2006; Huang, Knight, and Joshi 2006; Galley et al. (RNN) based translation model. Like the previous SMT 2006; Zhang et al. 2008), perform the decoding process and models, the RNN-based model induces the translation rules generate the translation result by compositing a set of translation from the bitexts. Unlike them, the RNN-based model learns rules which are associated with high probabilities. The how to represent each lexical translation rule with two compact probabilities of the translation rules (e.g. the phrasal translation semantic vectors, and learns how to perform decoding probabilities and the lexical weights in phrase-based using the merging type (swap or monotone) dependent recursive and formal syntax-based models) are all computed based on neural networks that attempt to find the best translation the cooccurrence statistics of the rule's source-and targetsides candidate having the minimal semantic gap with the source in the bilingual corpus.

Probabilistic topic models have been widely used for sentiment analysis. However, most of existing topic methods only model the sentiment text, but do not consider the user, who expresses the sentiment, and the item, which the sentiment is expressed on. Since different users may use different sentiment expressions for different items, we argue that it is better to incorporate the user and item information into the topic model for sentiment analysis. In this paper, we propose a new Supervised User-Item based Topic model, called SUIT model, for sentiment analysis. It can simultaneously utilize the textual topic and latent user-item factors. Our proposed method uses the tensor outer product of text topic proportion vector, user latent factor and item latent factor to model the sentiment label generalization. Extensive experiments are conducted on two datasets: review dataset and microblog dataset. The results demonstrate the advantages of our model. It shows significant improvement compared with supervised topic models and collaborative filtering methods.

Keyphrases for a document concisely describe the document using a small set of phrases. Keyphrases were previously shown to improve several document processing and retrieval tasks. In this work, we study keyphrase extraction from research papers by leveraging citation networks. We propose CiteTextRank for keyphrase extraction from research articles, a graph-based algorithm that incorporates evidence from both a document's content as well as the contexts in which the document is referenced within a citation network. Our model obtains significant improvements over the state-of-the-art models for this task. Specifically, on several datasets of research papers, CiteTextRank improves precision at rank 1 by as much as 9-20% over state-of-the-art baselines.

State-of-the-art approaches to Chinese zero pronoun resolution are supervised, requiring training documents with manually resolved zero pronouns. To eliminate the reliance on annotated data, we propose an unsupervised approach to this task. Underlying our approach is the novel idea of employing a model trained on manually resolved overt pronouns to resolve zero pronouns. Experimental results on the OntoNotes 5.0 corpus are encouraging: our unsupervised model surpasses its supervised counterparts in performance.

Cross-lingual text classification is the task of assigning labels to observed documents in a label-scarce target language domain by using a prediction model trained with labeled documents from a label-rich source language domain. Cross-lingual text classification is popularly studied in natural language processing area to reduce the expensive manual annotation effort required in the target language domain. In this work, we propose a novel semi-supervised representation learning approach to address this challenging task by inducing interlingual features via semi-supervised matrix completion. To evaluate the proposed learning technique, we conduct extensive experiments on eighteen cross language sentiment classification tasks with four different languages. The empirical results demonstrate the efficacy of the proposed approach, and show it outperforms a number of related cross-lingual learning methods.

In the field of NLP, most of the existing domain adaptation studies belong to the feature-based adaptation, while the research of instance-based adaptation is very scarce. In this work, we propose a new instance-based adaptation model, called in-target-domain logistic approximation (ILA). In ILA, we adapt the source-domain data to the target domain by a logistic approximation. The normalized in-target-domain probability is assigned as an instance weight to each of the source-domain training data. An instance-weighted classification model is trained finally for the cross-domain classification problem. Compared to the previous techniques, ILA conducts instance adaptation in a dimensionality-reduced linear feature space to ensure efficiency in high-dimensional NLP tasks. The instance weights in ILA are learnt by leveraging the criteria of both maximum likelihood and minimum statistical distance. The empirical results on two NLP tasks including text categorization and sentiment classification show that our ILA model beats the state-of-the-art instance adaptation methods significantly, in cross-domain classification accuracy, parameter stability and computational efficiency.

In web search, users queries are formulated using only few terms and term-matching retrieval functions could fail at retrieving relevant documents. Given a user query, the technique of query expansion (QE) consists in selecting related terms that could enhance the likelihood of retrieving relevant documents. Selecting such expansion terms is challenging and requires a computational framework capable of encoding complex semantic relationships. In this paper, we propose a novel method for learning, in a supervised way, semantic representations for words and phrases. By embedding queries and documents in special matrices, our model disposes of an increased representational power with respect to existing approaches adopting a vector representation. We show that our model produces high-quality query expansion terms. Our expansion increase IR measures beyond expansion from current word-embeddings models and well-established traditional QE methods.

Scripts have been proposed to model the stereotypical event sequences found in narratives. They can be applied to make a variety of inferences including fillinggaps in the narratives and resolving ambiguous references. This paper proposes the first formal frameworkfor scripts based on Hidden Markov Models (HMMs). Our framework supports robust inference and learning algorithms, which are lacking in previous clustering models. We develop an algorithm for structure andparameter learning based on Expectation Maximizationand evaluate it on a number of natural datasets. The results show that our algorithm is superior to several informed baselines for predicting missing events in partialobservation sequences.