Subword lexical modelling for speech recognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 155-160).by Raymond Lau.Ph.D

Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices. Our experiments show that ToT significantly enhances language models' problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, our method achieved a success rate of 74%. Code repo with all prompts: https://github.com/princeton-nlp/tree-of-thought-llm.Comment: NeurIPS 2023 camera ready version. Code repo with all prompts: https://github.com/princeton-nlp/tree-of-thought-ll

Efficient Non-deterministic Search in Structured Prediction: A Case Study on Syntactic Parsing

Non-determinism occurs naturally in many search-based machine learning and natural language processing (NLP) problems. For example, the goal of parsing is to construct the syntactic tree structure of a sentence given a grammar. Agenda-based parsing is a dynamic programming approach to find the most likely syntactic tree of a sentence according to a probabilistic grammar. A chart is used to maintain all the possible subtrees for different spans in the sentence and an agenda is used to rank all the constituents. The parser chooses only one constituent from the agenda per step. Non-determinism occurs naturally in agenda-based parsing since the new constituent is often built by combining items from a few steps earlier. Unfortunately, like most other problems in NLP, the size of the search space is huge and exhaustive search is impossible. However, users expect a fast and accurate system. In this dissertation, I focus on the question of ``Why, when, and how shall we take advantage of non-determinism?'' and show its efficacy to improve the parser in terms of speed and/or accuracy. Existing approaches like search-based imitation learning or reinforcement learning methods have different limitations when it comes to a large NLP system. The solution proposed in this dissertation is ``We should train the system non-deterministically and test it deterministically if possible.'' and I also show that ``it is better to learn with oracles than simple heuristics''. We start by solving a generic Markov Decision Process with a non-deterministic agent. We show its theoretical convergence guarantees and verify its efficiency on maze solving problems. Then we focus on agenda-based parsing. To re-prioritize the parser, we model a decoding problem as a Markov Decision Process with a large state/action space. We discuss the advantages/disadvantages of existing techniques and propose a hybrid reinforcement/apprenticeship learning algorithm to trade off speed and accuracy. We also propose to use a dynamic pruner with features that depend on the run-time status of the chart and agenda and analyze the importance of those features in the pruning classification. Our models show comparable results with respect to start-of-the-art strategies

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

Syntax-based machine translation using dependency grammars and discriminative machine learning

Machine translation underwent huge improvements since the groundbreaking introduction of statistical methods in the early 2000s, going from very domain-specific systems that still performed relatively poorly despite the painstakingly crafting of thousands of ad-hoc rules, to general-purpose systems automatically trained on large collections of bilingual texts which manage to deliver understandable translations that convey the general meaning of the original input. These approaches however still perform quite below the level of human translators, typically failing to convey detailed meaning and register, and producing translations that, while readable, are often ungrammatical and unidiomatic. This quality gap, which is considerably large compared to most other natural language processing tasks, has been the focus of the research in recent years, with the development of increasingly sophisticated models that attempt to exploit the syntactical structure of human languages, leveraging the technology of statistical parsers, as well as advanced machine learning methods such as marging-based structured prediction algorithms and neural networks. The translation software itself became more complex in order to accommodate for the sophistication of these advanced models: the main translation engine (the decoder) is now often combined with a pre-processor which reorders the words of the source sentences to a target language word order, or with a post-processor that ranks and selects a translation according according to fine model from a list of candidate translations generated by a coarse model. In this thesis we investigate the statistical machine translation problem from various angles, focusing on translation from non-analytic languages whose syntax is best described by fluid non-projective dependency grammars rather than the relatively strict phrase-structure grammars or projectivedependency grammars which are most commonly used in the literature. We propose a framework for modeling word reordering phenomena between language pairs as transitions on non-projective source dependency parse graphs. We quantitatively characterize reordering phenomena for the German-to-English language pair as captured by this framework, specifically investigating the incidence and effects of the non-projectivity of source syntax and the non-locality of word movement w.r.t. the graph structure. We evaluated several variants of hand-coded pre-ordering rules in order to assess the impact of these phenomena on translation quality. We propose a class of dependency-based source pre-ordering approaches that reorder sentences based on a flexible models trained by SVMs and and several recurrent neural network architectures. We also propose a class of translation reranking models, both syntax-free and source dependency-based, which make use of a type of neural networks known as graph echo state networks which is highly flexible and requires extremely little training resources, overcoming one of the main limitations of neural network models for natural language processing tasks

Hybrid data-driven models of machine translation

Corpus-based approaches to Machine Translation (MT) dominate the MT research field today, with Example-Based MT (EBMT) and Statistical MT (SMT) representing two different frameworks within the data-driven paradigm. EBMT has always made use of both phrasal and lexical correspondences to produce high-quality translations. Early SMT models, on the other hand, were based on word-level correpsondences, but with the advent of more sophisticated phrase-based approaches, the line between EBMT and SMT has become increasingly blurred. In this thesis we carry out a number of translation experiments comparing the performance of the state-of-the-art marker-based EBMT system of Gough and Way (2004a, 2004b), Way and Gough (2005) and Gough (2005) against a phrase-based SMT (PBSMT) system built using the state-of-the-art PHARAOphHra se-based decoder (Koehn, 2004a) and employing standard phrasal extraction in euristics (Koehn et al., 2003). In additin e describe experiments investigating the possibility of combining elements of EBMT and SMT in order to create a hybrid data-driven model of MT capable of outperforming either approach from which it is derived. Making use of training and testlng data taken from a French-Enghsh translation memory of Sun Microsystems computer documentation, we find that while better results are seen when the PBSMT system is seeded with GIZA++ word- and phrasebased data compared to EBMT marker-based sub-sentential alignments, in general improvements are obtained when combinations of this 'hybrid' data are used to construct the translation and probability models. While for the most part the baseline marker-based EBMT system outperforms any flavour of the PBSbIT systems constructed in these experiments, combining the data sets automatically induced by both GIZA++ and the EBMT system leads to a hybrid system which improves on the EBMT system per se for French-English. On a different data set, taken from the Europarl corpus (Koehn, 2005), we perform a number of experiments maklng use of incremental training data sizes of 78K, 156K and 322K sentence pairs. On this data set, we show that similar gains are to be had from constructing a hybrid 'statistical EBMT' system capable of outperforming the baseline EBMT system. This time around, although all 'hybrid' variants of the EBMT system fall short of the quality achieved by the baseline PBSMT system, merging elements of the marker-based and SMT data, as in the Sun Mzcrosystems experiments, to create a hybrid 'example-based SMT' system, outperforms the baseline SMT and EBMT systems from which it is derlved. Furthermore, we provide further evidence in favour of hybrid data-dr~ven approaches by adding an SMT target language model to all EBMT system variants and demonstrate that this too has a positive effect on translation quality. Following on from these findings we present a new hybrid data-driven MT architecture, together with a novel marker-based decoder which improves upon the performance of the marker-based EBMT system of Gough and Way (2004a, 2004b), Way and Gough (2005) and Gough (2005), and compares favourably with the stateof-the-art PHARAOH SMHT decoder (Koehn, 2004a)

The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction

With the advent of deep learning, research in many areas of machine learning is converging towards the same set of methods and models. For example, long short-term memory networks are not only popular for various tasks in natural language processing (NLP) such as speech recognition, machine translation, handwriting recognition, syntactic parsing, etc., but they are also applicable to seemingly unrelated fields such as robot control, time series prediction, and bioinformatics. Recent advances in contextual word embeddings like BERT boast with achieving state-of-the-art results on 11 NLP tasks with the same model. Before deep learning, a speech recognizer and a syntactic parser used to have little in common as systems were much more tailored towards the task at hand. At the core of this development is the tendency to view each task as yet another data mapping problem, neglecting the particular characteristics and (soft) requirements tasks often have in practice. This often goes along with a sharp break of deep learning methods with previous research in the specific area. This work can be understood as an antithesis to this paradigm. We show how traditional symbolic statistical machine translation models can still improve neural machine translation (NMT) while reducing the risk for common pathologies of NMT such as hallucinations and neologisms. Other external symbolic models such as spell checkers and morphology databases help neural grammatical error correction. We also focus on language models that often do not play a role in vanilla end-to-end approaches and apply them in different ways to word reordering, grammatical error correction, low-resource NMT, and document-level NMT. Finally, we demonstrate the benefit of hierarchical models in sequence-to-sequence prediction. Hand-engineered covering grammars are effective in preventing catastrophic errors in neural text normalization systems. Our operation sequence model for interpretable NMT represents translation as a series of actions that modify the translation state, and can also be seen as derivation in a formal grammar.EPSRC grant EP/L027623/1 EPSRC Tier-2 capital grant EP/P020259/

SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING

Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP

Parsing Inside-Out

The inside-outside probabilities are typically used for reestimating Probabilistic Context Free Grammars (PCFGs), just as the forward-backward probabilities are typically used for reestimating HMMs. I show several novel uses, including improving parser accuracy by matching parsing algorithms to evaluation criteria; speeding up DOP parsing by 500 times; and 30 times faster PCFG thresholding at a given accuracy level. I also give an elegant, state-of-the-art grammar formalism, which can be used to compute inside-outside probabilities; and a parser description formalism, which makes it easy to derive inside-outside formulas and many others.Comment: Ph.D. Thesis, 257 pages, 40 postscript figure