Phrase-based Image Captioning

Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing. In this paper, we present a simple model that is able to generate descriptive sentences given a sample image. This model has a strong focus on the syntax of the descriptions. We train a purely bilinear model that learns a metric between an image representation (generated from a previously trained Convolutional Neural Network) and phrases that are used to described them. The system is then able to infer phrases from a given image sample. Based on caption syntax statistics, we propose a simple language model that can produce relevant descriptions for a given test image using the phrases inferred. Our approach, which is considerably simpler than state-of-the-art models, achieves comparable results in two popular datasets for the task: Flickr30k and the recently proposed Microsoft COCO

280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification

We propose a simple, yet effective, approach towards inducing multilingual taxonomies from Wikipedia. Given an English taxonomy, our approach leverages the interlanguage links of Wikipedia followed by character-level classifiers to induce high-precision, high-coverage taxonomies in other languages. Through experiments, we demonstrate that our approach significantly outperforms the state-of-the-art, heuristics-heavy approaches for six languages. As a consequence of our work, we release presumably the largest and the most accurate multilingual taxonomic resource spanning over 280 languages

Rehabilitation of Count-based Models for Word Vector Representations

Recent works on word representations mostly rely on predictive models. Distributed word representations (aka word embeddings) are trained to optimally predict the contexts in which the corresponding words tend to appear. Such models have succeeded in capturing word similarties as well as semantic and syntactic regularities. Instead, we aim at reviving interest in a model based on counts. We present a systematic study of the use of the Hellinger distance to extract semantic representations from the word co-occurence statistics of large text corpora. We show that this distance gives good performance on word similarity and analogy tasks, with a proper type and size of context, and a dimensionality reduction based on a stochastic low-rank approximation. Besides being both simple and intuitive, this method also provides an encoding function which can be used to infer unseen words or phrases. This becomes a clear advantage compared to predictive models which must train these new words.Comment: A. Gelbukh (Ed.), Springer International Publishing Switzerlan

N-gram-Based Low-Dimensional Representation for Document Classification

The bag-of-words (BOW) model is the common approach for classifying documents, where words are used as feature for training a classifier. This generally involves a huge number of features. Some techniques, such as Latent Semantic Analysis (LSA) or Latent Dirichlet Allocation (LDA), have been designed to summarize documents in a lower dimension with the least semantic information loss. Some semantic information is nevertheless always lost, since only words are considered. Instead, we aim at using information coming from n-grams to overcome this limitation, while remaining in a low-dimension space. Many approaches, such as the Skip-gram model, provide good word vector representations very quickly. We propose to average these representations to obtain representations of n-grams. All n-grams are thus embedded in a same semantic space. A K-means clustering can then group them into semantic concepts. The number of features is therefore dramatically reduced and documents can be represented as bag of semantic concepts. We show that this model outperforms LSA and LDA on a sentiment classification task, and yields similar results than a traditional BOW-model with far less features.Comment: Accepted as a workshop contribution at ICLR 201

Generating Video Descriptions with Topic Guidance

Generating video descriptions in natural language (a.k.a. video captioning) is a more challenging task than image captioning as the videos are intrinsically more complicated than images in two aspects. First, videos cover a broader range of topics, such as news, music, sports and so on. Second, multiple topics could coexist in the same video. In this paper, we propose a novel caption model, topic-guided model (TGM), to generate topic-oriented descriptions for videos in the wild via exploiting topic information. In addition to predefined topics, i.e., category tags crawled from the web, we also mine topics in a data-driven way based on training captions by an unsupervised topic mining model. We show that data-driven topics reflect a better topic schema than the predefined topics. As for testing video topic prediction, we treat the topic mining model as teacher to train the student, the topic prediction model, by utilizing the full multi-modalities in the video especially the speech modality. We propose a series of caption models to exploit topic guidance, including implicitly using the topics as input features to generate words related to the topic and explicitly modifying the weights in the decoder with topics to function as an ensemble of topic-aware language decoders. Our comprehensive experimental results on the current largest video caption dataset MSR-VTT prove the effectiveness of our topic-guided model, which significantly surpasses the winning performance in the 2016 MSR video to language challenge.Comment: Appeared at ICMR 201

Word Embeddings for Natural Language Processing

Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for capturing both syntactic and semantic information about words. Word embeddings, when used as the underlying input representation, have been shown to be a great asset for a large variety of natural language processing (NLP) tasks. Recent techniques to obtain such word embeddings are mostly based on neural network language models (NNLM). In such systems, the word vectors are randomly initialized and then trained to predict optimally the contexts in which the corresponding words tend to appear. Because words occurring in similar contexts have, in general, similar meanings, their resulting word embeddings are semantically close after training. However, such architectures might be challenging and time-consuming to train. In this thesis, we are focusing on building simple models which are fast and efficient on large-scale datasets. As a result, we propose a model based on counts for computing word embeddings. A word co-occurrence probability matrix can easily be obtained by directly counting the context words surrounding the vocabulary words in a large corpus of texts. The computation can then be drastically simplified by performing a Hellinger PCA of this matrix. Besides being simple, fast and intuitive, this method has two other advantages over NNLM. It first provides a framework to infer unseen words or phrases. Secondly, all embedding dimensions can be obtained after a single Hellinger PCA, while a new training is required for each new size with NNLM. We evaluate our word embeddings on classical word tagging tasks and show that we reach similar performance than with neural network based word embeddings. While many techniques exist for computing word embeddings, vector space models for phrases remain a challenge. Still based on the idea of proposing simple and practical tools for NLP, we introduce a novel model that jointly learns word embeddings and their summation. Sequences of words (i.e. phrases) with different sizes are thus embedded in the same semantic space by just averaging word embeddings. In contrast to previous methods which reported a posteriori some compositionality aspects by simple summation, we simultaneously train words to sum, while keeping the maximum information from the original vectors. These word and phrase embeddings are then used in two different NLP tasks: document classification and sentence generation. Using such word embeddings as inputs, we show that good performance is achieved in sentiment classification of short and long text documents with a convolutional neural network. Finding good compact representations of text documents is crucial in classification systems. Based on the summation of word embeddings, we introduce a method to represent documents in a low-dimensional semantic space. This simple operation, along with a clustering method, provides an efficient framework for adding semantic information to documents, which yields better results than classical approaches for classification. Simple models for sentence generation can also be designed by leveraging such phrase embeddings. We propose a phrase-based model for image captioning which achieves similar results than those obtained with more complex models. Not only word and phrase embeddings but also embeddings for non-textual elements can be helpful for sentence generation. We, therefore, explore to embed table elements for generating better sentences from structured data. We experiment this approach with a large-scale dataset of biographies, where biographical infoboxes were available. By parameterizing both words and fields as vectors (embeddings), we significantly outperform a classical model