Enhancing natural language understanding using meaning representation and deep learning

Natural Language Understanding (NLU) is one of the complex tasks in artificial intelligence. Machine learning was introduced to address the complex and dynamic nature of natural language. Deep learning gained popularity within the NLU community due to its capability of learning features directly from data, as well as learning from the dynamic nature of natural language. Furthermore, deep learning has shown to be able to learn the hidden feature(s) automatically and outperform most of the other machine learning approaches for NLU. Deep learning models require natural language inputs to be converted to vectors (word embedding). Word2Vec and GloVe are word embeddings which are designed to capture the analogy context-based statistics and provide lexical relations on words. Using the context-based statistical approach does not capture the prior knowledge required to understand language combined with words. Although a deep learning model receives word embedding, language understanding requires Reasoning, Attention and Memory (RAM). RAM are key factors in understanding language. Current deep learning models focus either on reasoning, attention or memory. In order to properly understand a language however, all three factors of RAM should be considered. Also, a language normally has a long sequence. This long sequence creates dependencies which are required in order to understand a language. However, current deep learning models, which are developed to hold longer sequences, either forget or get affected by the vanishing or exploding gradient descent. In this thesis, these three main areas are of focus. A word embedding technique, which integrates analogy context-based statistical and semantic relationships, as well as extracts from a knowledge base to hold enhanced meaning representation, is introduced. Also, a Long Short-Term Reinforced Memory (LSTRM) network is introduced. This addresses RAM and is validated by testing on question answering data sets which require RAM. Finally, a Long Term Memory Network (LTM) is introduced to address language modelling. Good language modelling requires learning from long sequences. Therefore, this thesis demonstrates that integrating semantic knowledge and a knowledge base generates enhanced meaning and deep learning models that are capable of achieving RAM and long-term dependencies so as to improve the capability of NLU

Word Sense Consistency in Statistical and Neural Machine Translation

Different senses of source words must often be rendered by different words in the target language when performing machine translation (MT). Selecting the correct translation of polysemous words can be done based on the contexts of use. However, state-of-the-art MT algorithms generally work on a sentence-by-sentence basis that ignores information across sentences. In this thesis, we address this problem by studying novel contextual approaches to reduce source word ambiguity in order to improve translation quality. The thesis consists of two parts: the first part is devoted to methods for correcting ambiguous word translations by enforcing consistency across sentences, and the second part investigates sense-aware MT systems that address the ambiguity problem for each word. In the first part, we propose to reduce word ambiguity by using lexical consistency, starting from the one-sense-per-discourse hypothesis. If a polysemous word appears multiple times in a discourse, it is likely that occurrences will share the same sense. We first improve the translation of polysemous nouns (Y) in the case when a previous occurrence of a noun as the head of a compound noun phrase (XY) is available in a text. Experiments on two language pairs show that the translations of the targeted polysemous nouns are significantly improved. As compound pairs X Y /Y appear quite infrequently in texts, we extend our work by analysing the repetition of nouns which are not compounds. We propose a method to decide whether two occurrences of the same noun in a source text should be translated consistently. We design a classifier to predict translation consistency based on syntactic and semantic features. We integrate the classifiersâ output into MT. We experiment on two language pairs and show that our method closes up to 50% of the gap in BLEU scores between the baseline and an oracle classifier. In the second part of the thesis, we design sense-aware MT systems that (automatically) select the correct translations of ambiguous words by performing word sense disambiguation (WSD). We demonstrate that WSD can improve MT by widening the source context considered when modeling the senses of potentially ambiguous words. We first design three adaptive clustering algorithms, respectively based on k-means, Chinese restaurant process and random walk. For phrase-based statistical MT, we integrate the sense knowledge as an additional feature through a factored model and show that the combination improves the translation from English to five other languages. As the sense integration appears promising for SMT, we also transfer this approach to the newer neural MT models, which are now state of the art. However, unlike SMT, for which it is easier to use linguistic features, NMT uses vectors for word generation and traditional feature incorporation does not work here. We design a sense-aware NMT model that jointly learns the sense knowledge using an attention-based sense selection mechanism and concatenates the learned sense vectors with word vectors during encoding . Such a concatenation outperforms several baselines. The improvements are significant over both overall and analysed ambiguous words over the same language pairs we experiment with SMT. Overall, the thesis proves that lexical consistency and WSD are practical and workable solutions that lead to global improvements in translation in ranges of 0.2 to 1.5 BLEU score