Multilingual Word Sense Induction to Improve Web Search Result Clustering

In [12] a novel approach to Web search result clustering based on Word Sense Induction, i.e. the automatic discovery of word senses from raw text was presented; key to the proposed approach is the idea of, first, automatically in- ducing senses for the target query and, second, clustering the search results based on their semantic similarity to the word senses induced. In [1] we proposed an innovative Word Sense Induction method based on multilingual data; key to our approach was the idea that a multilingual context representation, where the context of the words is expanded by considering its translations in different languages, may im- prove the WSI results; the experiments showed a clear per- formance gain. In this paper we give some preliminary ideas to exploit our multilingual Word Sense Induction method to Web search result clustering

Improving Statistical Machine Translation Using Comparable Corpora

With thousands of languages in the world, and the increasing speed and quantity of information being distributed across the world, automatic translation between languages by computers, Machine Translation (MT), has become an increasingly important area of research. State-of-the-art MT systems rely not upon hand-crafted translation rules written by human experts, but rather on learned statistical models that translate a source language to a target language. These models are typically generated from large, parallel corpora containing copies of text in both the source and target languages. The co-occurrence of words across languages in parallel corpora allows the creation of translation rules that specify the probability of translating words or phrases from one language to the other. Monolingual corpora, containing text only in one language--primarily the target language--are not used to model the translation process, but are used to better model the structure of the target language. Unlike parallel data, which require expensive human translators to generate, monolingual data are cheap and widely available. Similar topics and events to those in a source document that is being translated often occur in documents in a comparable monolingual corpus. In much the same way that a human translator would use world knowledge to aid translation, the MT system may be able to use these relevant documents from comparable corpora to guide translation by biasing the translation system to produce output more similar to the relevant documents. This thesis seeks to answer the following questions: (1) Is it possible to improve a modern, state-of-the-art translation system by biasing the MT output to be more similar to relevant passages from comparable monolingual text? (2) What level of similarity is necessary to exploit these techniques? (3) What is the nature of the relevant passages that are needed during the application of these techniques? To answer these questions, this thesis describes a method for generating new translation rules from monolingual data specifically targeted for the document that is being translated. Rule generation leverages the existing translation system and topical overlap between the foreign source text and the monolingual text, and unlike regular translation rule generation does not require parallel text. For each source document to be translated, potentially comparable documents are selected from the monolingual data using cross-lingual information retrieval. By biasing the MT system towards the selected relevant documents and then measuring the similarity of the biased output to the relevant documents using Translation Edit Rate Plus (TERp), it is possible to identify sub-sentential regions of the source and comparable documents that are possible translations of each other. This process results in the generation of new translation rules, where the source side is taken from the document to be translated and the target side is fluent target language text taken from the monolingual data. The use of these rules results in improvements over a state-of-the-art statistical translation system. These techniques are most effective when there is a high degree of similarity between the source and relevant passages--such as when they report on the same new stories--but some benefit, approximately half, can be achieved when the passages are only historically or topically related. The discovery of the feasibility of improving MT by using comparable passages to bias MT output provides a basis for future investigation on problems of this type. Ultimately, the goal is to provide a framework within which translation rules may be generated without additional parallel corpora, thus allowing researchers to test longstanding hypotheses about machine translation in the face of scarce parallel resources

Disentangled Code Representation Learning for Multiple Programming Languages

Developing effective distributed representations of source code is fundamental yet challenging for many software engineering tasks such as code clone detection, code search, code translation and transformation. However, current code embedding approaches that represent the semantic and syntax of code in a mixed way are less interpretable and the resulting embedding can not be easily generalized across programming languages. In this paper, we propose a disentangled code representation learning approach to separate the semantic from the syntax of source code under a multi-programming-language setting, obtaining better interpretability and generalizability. Specially, we design three losses dedicated to the characteristics of source code to enforce the disentanglement effectively. We conduct comprehensive experiments on a real-world dataset composed of programming exercises implemented by multiple solutions that are semantically identical but grammatically distinguished. The experimental results validate the superiority of our proposed disentangled code representation, compared to several baselines, across three types of downstream tasks, i.e., code clone detection, code translation, and code-to-code search

Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow

For tasks like code synthesis from natural language, code retrieval, and code summarization, data-driven models have shown great promise. However, creating these models require parallel data between natural language (NL) and code with fine-grained alignments. Stack Overflow (SO) is a promising source to create such a data set: the questions are diverse and most of them have corresponding answers with high-quality code snippets. However, existing heuristic methods (e.g., pairing the title of a post with the code in the accepted answer) are limited both in their coverage and the correctness of the NL-code pairs obtained. In this paper, we propose a novel method to mine high-quality aligned data from SO using two sets of features: hand-crafted features considering the structure of the extracted snippets, and correspondence features obtained by training a probabilistic model to capture the correlation between NL and code using neural networks. These features are fed into a classifier that determines the quality of mined NL-code pairs. Experiments using Python and Java as test beds show that the proposed method greatly expands coverage and accuracy over existing mining methods, even when using only a small number of labeled examples. Further, we find that reasonable results are achieved even when training the classifier on one language and testing on another, showing promise for scaling NL-code mining to a wide variety of programming languages beyond those for which we are able to annotate data.Comment: MSR '1