This paper describes a statistical approach to generation of Chinese classical poetry and proposes a novel method to automatically evaluate poems. The system accepts a set of keywords representing the writing intents from a writer and generates sentences one by one to form a completed poem. A statistical machine translation (SMT) system is applied to generate new sentences, given the sentences generated previously. For each line of sentence a specific model specially trained for that line is used, as opposed to using a single model for all sentences. To enhance the coherence of sentences on every line, a coherence model using mutual information is applied to select candidates with better consistency with previous sentences. In addition, we demonstrate the effectiveness of the BLEU metric for evaluation with a novel method of generating diverse references.

This article is the first report in the best of AI in Japan series. This series will focus on the prominent accomplishments made in the AI field, not only the research and development but also the AI-related events in society. As the first in the forthcoming series, this opening article features a historical background and the contemporary AI-research activities in Japan. It then highlights some recent prominent results from the industry. Finally, a future perspective is given.

We propose a novel language-independent approach for improving machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resource-poor source language X_1 into a resource-rich language Y given a bi-text containing a limited number of parallel sentences for X_1-Y and a larger bi-text for X_2-Y for some resource-rich language X_2 that is closely related to X_1. This is achieved by taking advantage of the opportunities that vocabulary overlap and similarities between the languages X_1 and X_2 in spelling, word order, and syntax offer: (1) we improve the word alignments for the resource-poor language, (2) we further augment it with additional translation options, and (3) we take care of potential spelling differences through appropriate transliteration. The evaluation for Indonesian- >English using Malay and for Spanish -> English using Portuguese and pretending Spanish is resource-poor shows an absolute gain of up to 1.35 and 3.37 BLEU points, respectively, which is an improvement over the best rivaling approaches, while using much less additional data. Overall, our method cuts the amount of necessary "real'' training data by a factor of 2--5.

The second was the propensity of the computing industry toward more lucrative assignments in the service sector. Both these factors are changing, not least because leading international software companies have set up research and development centers in the country. Computer science education established itself in India in the early 1980s when the Indian Institutes of Technology (IITs) set up computer science departments and started offering undergraduate programs in the discipline. Research in artificial intelligence took off soon afterward when the government of India launched the Knowledge Based Computing Systems (KBCS) program in conjunction with the United Nations Development Program (Saint-Dizier 1991). A number of nodal centers were set up to focus on different areas of research including expert systems (IIT Madras), speech processing (Tata Institue of Fundamental Research), parallel processing (Indian Institute for Science), image processing (Indian Statistical Institute), and natural language processing (Center for Development of Advanced Computing).

Large bilingual parallel texts (also known as bitexts) are usually stored in a compressed form, and previous work has shown that they can be more efficiently compressed if the fact that the two texts are mutual translations is exploited. For example, a bitext can be seen as a sequence of biwords ---pairs of parallel words with a high probability of co-occurrence--- that can be used as an intermediate representation in the compression process. However, the simple biword approach described in the literature can only exploit one-to-one word alignments and cannot tackle the reordering of words. We therefore introduce a generalization of biwords which can describe multi-word expressions and reorderings. We also describe some methods for the binary compression of generalized biword sequences, and compare their performance when different schemes are applied to the extraction of the biword sequence. In addition, we show that this generalization of biwords allows for the implementation of an efficient algorithm to look on the compressed bitext for words or text segments in one of the texts and retrieve their counterpart translations in the other text ---an application usually referred to as translation spotting--- with only some minor modifications in the compression algorithm.

Quality assurance remains a key topic in human computation research field. Prior work indicates independent agreement is effective for low difficulty tasks, but has limitations. This paper addresses this problem by proposing a tournament selection based quality control process. The experimental results from this paper show that the human are better at identifying the correct answers than producing them themselves.

In this paper we present a novel user interface that integrates two popular approaches to language translation for travelers allowing multimodal communication between the parties involved: the picture-book, in which the user simply points to multiple picture icons representing what they want to say, and the statistical machine translation system that can translate arbitrary word sequences. Our prototype system tightly couples both processes within a translation framework that inherits many of the the positive features of both approaches, while at the same time mitigating their main weaknesses. Our system differs from traditional approaches in that its mode of input is a sequence of pictures, rather than text or speech. Text in the source language is generated automatically, and is used as a detailed representation of the intended meaning. The picture sequence which not only provides a rapid method to communicate basic concepts but also gives a `second opinion' on the machine transition output that catches machine translation errors and allows the users to retry the translation, avoiding misunderstandings.

To support more precise query translation for English-Chinese Bi-Directional Cross-Language Information Retrieval (CLIR), we have developed a novel framework by integrating a semantic network to characterize the correlations between multiple inter-related text terms of interest and learn their inter-related statistical query translation models. First, a semantic network is automatically generated from large-scale English-Chinese bilingual parallel corpora to characterize the correlations between a large number of text terms of interest. Second, the semantic network is exploited to learn the statistical query translation models for such text terms of interest. Finally, these inter-related query translation models are used to translate the queries more precisely and achieve more effective CLIR. Our experiments on a large number of official public data have obtained very positive results.

Consistent word selection in machine translation is currently realized by resolving word sense ambiguity through the context of a single sentence or neighboring sentences. However, consistent word selection over the whole article has yet to be achieved. Consistency over the whole article is extremely important when applying machine translation to collectively developed documents like Wikipedia. In this paper, we propose to consider constraints between words in the whole article based on their semantic relatedness and contextual distance. The proposed method is successfully implemented in both statistical and rule-based translators. We evaluate those systems by translating 100 articles in the English Wikipedia into Japanese. The results show that the ratio of appropriate word selection for common nouns increased to around 75% with our method, while it was around 55% without our method.

We argue for an alternative paradigm in evaluating machine translation quality that is strongly empirical but more accurately reflects the utility of translations, by returning to a representational foundation based on AI oriented lexical semantics, rather than the superficial flat n-gram and string representations recently dominating the field. Driven by such metrics as BLEU and WER, current SMT frequently produces unusable translations where the semantic event structure is mistranslated: who did what to whom, when, where, why, and how? We argue that it is time for a new generation of more “intelligent” automatic and semi-automatic metrics, based clearly on getting the structure right at the lexical semantics level. We show empirically that it is possible to use simple PropBank style semantic frame representations to surpass all currently widespread metrics' correlation to human adequacy judgments, including even HTER. We also show that replacing human annotators with automatic semantic role labeling still yields much of the advantage of the approach. We combine the best of both worlds: from an SMT perspective, we provide superior yet low-cost quantitative objective functions for translation quality; and yet from an AI perspective, we regain the representational transparency and clear reflection of semantic utility of structural frame-based knowledge representations.