The aiXplain SDK 1 is an open-source Python toolkit which aims to simplify the wide and complex ecosystem of AI resources.The toolkit enables access to a wide selection of AI assets, including datasets, models, and metrics, from both academic and commercial sources, which can be selected, executed and evaluated in one place through different services in a standardized format with consistent documentation provided.The study showcases the potential of the proposed toolkit with different code examples and by using it on a user journey where state-of-the-art Large Language Models are fine-tuned on instruction prompt datasets, outperforming their base versions.

The performance of Machine Translation (MT) systems varies significantly with inputs of diverging features such as topics, genres, and surface properties. Though there are many MT evaluation metrics that generally correlate with human judgments, they are not directly useful in identifying specific shortcomings of MT systems. In this demo, we present a benchmarking interface that enables improved evaluation of specific MT systems in isolation or multiple MT systems collectively by quantitatively evaluating their performance on many tasks across multiple domains and evaluation metrics. Further, it facilitates effective debugging and error analysis of MT output via the use of dynamic filters that help users hone in on problem sentences with specific properties, such as genre, topic, sentence length, etc. The interface can be extended to include additional filters such as lexical, morphological, and syntactic features. Aside from helping debug MT output, it can also help in identifying problems in reference translations and evaluation metrics.

The advent of advanced modeling for general machine learning, and in particular computer vision, speech recognition and natural language processing, the applications of AI is enabling classical businesses to reinvent themselves, and new business fields to arise which were even not imaginable a few years back. Hassan will present some of these use cases, and dive into some in more detail, showing where current and future AI/ML technology is accelerating innovation.

The performance of automatic speech recognition system degrades significantly when the incoming audio differs from training data. Maximum likelihood linear regression has been widely used for unsupervised adaptation, usually in a multiple-pass recognition process. Here we present a novel adaptation framework for which the offline, supervised, high-quality adaptation is applied to clustered channel/speaker conditions that are defined with automatic and manual clustering of the training data. Upon online recognition, each speech segment is classified into one of the training clusters in an unsupervised way, and the corresponding top acoustic models are used for recognition. Recognition lattice outputs are combined. Experiments are performed on the Wall Street Journal data, and a 37.5% relative reduction of Word Error Rate is reported. The proposed approach is also compared with a general speaker adaptive training approach.

Keyboard layout errors and homoglyphs in cross-language queries impact our abil-ity to correctly interpret user informa-tion needs and offer relevant results. We present a machine learning approach to correcting these errors, based largely on character-level n-gram features. We demonstrate superior performance over rule-based methods, as well as a signif-icant reduction in the number of queries that yield null search results. 1

We describe three approaches to statistical translation and present experimental results. The statistical translation approach uses two types of information: a translation model and a language model. The language model used is a bigram or general m-gram model. The translation model is decomposed into a lexical model and an alignment model. There are three approaches that are presented and tested in detail: the quasi-monotone alignment approach, the inverted alignment approach, and the alignment template approach. For each of these three approaches, a suitable search method is presented. The system has been tested on a limited-domain spoken-language task for which a bilingual corpus is available: the Verbmobil task (German-English, 7000-word vocabulary). We present experimental results for each of the three approaches. The experimental tests were performed on both the text transcription and the speech recognizer output.

In this paper, we describe a fast search algorithm for statistical translation based on dynamic programming (DP) and present experimental results.The approach is based on the assumption that the word alignment is monotone with respect to the word order in both languages.To reduce the search eort for this approach, we introduce two methods: an acceleration technique to eciently compute the dynamic programming recursion equation and a beam search strategy as used in speech recognition.The experimental tests carried out on the Verbmobil corpus showed that the search space, measured by the number of translation hypotheses, is reduced by a factor of about 230 without aecting the translation performance.