LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model

Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM. Syntactic structure information, a type of effective feature which has been extensively utilized in IE community, should also be beneficial to UIE. In this work, we propose a novel structure-aware GLM, fully unleashing the power of syntactic knowledge for UIE. A heterogeneous structure inductor is explored to unsupervisedly induce rich heterogeneous structural representations by post-training an existing GLM. In particular, a structural broadcaster is devised to compact various latent trees into explicit high-order forests, helping to guide a better generation during decoding. We finally introduce a task-oriented structure fine-tuning mechanism, further adjusting the learned structures to most coincide with the end-task's need. Over 12 IE benchmarks across 7 tasks our system shows significant improvements over the baseline UIE system. Further in-depth analyses show that our GLM learns rich task-adaptive structural bias that greatly resolves the UIE crux, the long-range dependence issue and boundary identifying. Source codes are open at https://github.com/ChocoWu/LasUIE.Comment: NeurIPS2022 conference pape

A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena

Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials. To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling. We then question why some approaches are more successful than others in different language pairs. We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic

Decision Tree-based Syntactic Language Modeling

Statistical Language Modeling is an integral part of many natural language processing applications, such as Automatic Speech Recognition (ASR) and Machine Translation. N-gram language models dominate the field, despite having an extremely shallow view of language---a Markov chain of words. In this thesis, we develop and evaluate a joint language model that incorporates syntactic and lexical information in a effort to ``put language back into language modeling.'' Our main goal is to demonstrate that such a model is not only effective but can be made scalable and tractable. We utilize decision trees to tackle the problem of sparse parameter estimation which is exacerbated by the use of syntactic information jointly with word context. While decision trees have been previously applied to language modeling, there has been little analysis of factors affecting decision tree induction and probability estimation for language modeling. In this thesis, we analyze several aspects that affect decision tree-based language modeling, with an emphasis on syntactic language modeling. We then propose improvements to the decision tree induction algorithm based on our analysis, as well as the methods for constructing forest models---models consisting of multiple decision trees. Finally, we evaluate the impact of our syntactic language model on large scale Speech Recognition and Machine Translation tasks. In this thesis, we also address a number of engineering problems associated with the joint syntactic language model in order to make it tractable. Particularly, we propose a novel decoding algorithm that exploits the decision tree structure to eliminate unnecessary computation. We also propose and evaluate an approximation of our syntactic model by word n-grams---the approximation that makes it possible to incorporate our model directly into the CDEC Machine Translation decoder rather than using the model for rescoring hypotheses produced using an n-gram model