15 research outputs found
Gap between theory and practice: noise sensitive word alignment in machine translation
Word alignment is to estimate a lexical translation probability p(e|f), or to estimate the correspondence g(e, f) where a function g outputs either 0 or 1, between a source word f and a target word e for given bilingual sentences. In practice, this formulation does not consider the existence of ‘noise’ (or outlier) which may cause problems depending on the corpus. N-to-m mapping objects, such as paraphrases, non-literal translations, and multiword
expressions, may appear as both noise and also as valid training data. From this perspective, this paper tries to answer the following two questions: 1) how to detect stable
patterns where noise seems legitimate, and 2) how to reduce such noise, where applicable, by supplying extra information as prior knowledge to a word aligner
Using Global Constraints and Reranking to Improve Cognates Detection
Global constraints and reranking have not been used in cognates detection
research to date. We propose methods for using global constraints by performing
rescoring of the score matrices produced by state of the art cognates detection
systems. Using global constraints to perform rescoring is complementary to
state of the art methods for performing cognates detection and results in
significant performance improvements beyond current state of the art
performance on publicly available datasets with different language pairs and
various conditions such as different levels of baseline state of the art
performance and different data size conditions, including with more realistic
large data size conditions than have been evaluated with in the past.Comment: 10 pages, 6 figures, 6 tables; published in the Proceedings of the
55th Annual Meeting of the Association for Computational Linguistics, pages
1983-1992, Vancouver, Canada, July 201
On Correcting Inputs: Inverse Optimization for Online Structured Prediction
Algorithm designers typically assume that the input data is correct, and then
proceed to find "optimal" or "sub-optimal" solutions using this input data.
However this assumption of correct data does not always hold in practice,
especially in the context of online learning systems where the objective is to
learn appropriate feature weights given some training samples. Such scenarios
necessitate the study of inverse optimization problems where one is given an
input instance as well as a desired output and the task is to adjust the input
data so that the given output is indeed optimal. Motivated by learning
structured prediction models, in this paper we consider inverse optimization
with a margin, i.e., we require the given output to be better than all other
feasible outputs by a desired margin. We consider such inverse optimization
problems for maximum weight matroid basis, matroid intersection, perfect
matchings, minimum cost maximum flows, and shortest paths and derive the first
known results for such problems with a non-zero margin. The effectiveness of
these algorithmic approaches to online learning for structured prediction is
also discussed.Comment: Conference version to appear in FSTTCS, 201
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
The Internet has enabled the creation of a growing number of large-scale
knowledge bases in a variety of domains containing complementary information.
Tools for automatically aligning these knowledge bases would make it possible
to unify many sources of structured knowledge and answer complex queries.
However, the efficient alignment of large-scale knowledge bases still poses a
considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a
simple algorithm for aligning knowledge bases with millions of entities and
facts. SiGMa is an iterative propagation algorithm which leverages both the
structural information from the relationship graph as well as flexible
similarity measures between entity properties in a greedy local search, thus
making it scalable. Despite its greedy nature, our experiments indicate that
SiGMa can efficiently match some of the world's largest knowledge bases with
high precision. We provide additional experiments on benchmark datasets which
demonstrate that SiGMa can outperform state-of-the-art approaches both in
accuracy and efficiency.Comment: 10 pages + 2 pages appendix; 5 figures -- initial preprin
Better word alignments with supervised ITG models
This work investigates supervised word align-ment methods that exploit inversion transduc-tion grammar (ITG) constraints. We con-sider maximum margin and conditional like-lihood objectives, including the presentation of a new normal form grammar for canoni-calizing derivations. Even for non-ITG sen-tence pairs, we show that it is possible learn ITG alignment models by simple relaxations of structured discriminative learning objec-tives. For efficiency, we describe a set of prun-ing techniques that together allow us to align sentences two orders of magnitude faster than naive bitext CKY parsing. Finally, we intro-duce many-to-one block alignment features, which significantly improve our ITG models. Altogether, our method results in the best re-ported AER numbers for Chinese-English and a performance improvement of 1.1 BLEU over GIZA++ alignments.
Accelerated Training of Max-Margin Markov Networks with Kernels
Abstract. Structured output prediction is an important machine learn-ing problem both in theory and practice, and the max-margin Markov network (M3N) is an effective approach. All state-of-the-art algorithms for optimizing M3N objectives take at least O(1/) number of iterations to find an accurate solution. [1] broke this barrier by proposing an excessive gap reduction technique (EGR) which converges in O(1/ iterations. However, it is restricted to Euclidean projections which con-sequently requires an intractable amount of computation for each iter-ation when applied to solve M3N. In this paper, we show that by ex-tending EGR to Bregman projection, this faster rate of convergence can be retained, and more importantly, the updates can be performed effi-ciently by exploiting graphical model factorization. Further, we design a kernelized procedure which allows all computations per iteration to be performed at the same cost as the state-of-the-art approaches.
Constrained word alignment models for statistical machine translation
Word alignment is a fundamental and crucial component in Statistical Machine Translation (SMT) systems. Despite the enormous progress made in the past two decades, this task remains an active research topic simply because the quality of word alignment is still far from optimal. Most state-of-the-art word alignment models are grounded on statistical learning theory treating word alignment as a general sequence alignment problem, where many linguistically motivated insights are not incorporated. In this thesis, we propose new word alignment models with linguistically motivated constraints in a bid to improve the quality of word alignment for Phrase-Based SMT systems (PB-SMT). We start the exploration with an investigation
into segmentation constraints for word alignment by proposing a novel algorithm, namely word packing, which is motivated by the fact that one concept expressed by one word in one language can frequently surface as a compound or
collocation in another language. Our algorithm takes advantage of the interaction between segmentation and alignment, starting with some segmentation for both the
source and target language and updating the segmentation with respect to the word alignment results using state-of-the-art word alignment models; thereafter a refined
word alignment can be obtained based on the updated segmentation. In this process, the updated segmentation acts as a hard constraint on the word alignment
models and reduces the complexity of the alignment models by generating more 1-to-1 correspondences through word packing. Experimental results show that this algorithm can lead to statistically significant improvements over the state-of-the-art word alignment models. Given that word packing imposes "hard" segmentation constraints on the word aligner, which is prone to introducing noise, we propose two
new word alignment models using syntactic dependencies as soft constraints. The first model is a syntactically enhanced discriminative word alignment model, where
we use a set of feature functions to express the syntactic dependency information encoded in both source and target languages. One the one hand, this model enjoys
great flexibility in its capacity to incorporate multiple features; on the other hand, this model is designed to facilitate model tuning for different objective functions.
Experimental results show that using syntactic constraints can improve the performance of the discriminative word alignment model, which also leads to better PB-SMT performance compared to using state-of-the-art word alignment models.
The second model is a syntactically constrained generative word alignment model, where we add in a syntactic coherence model over the target phrases in the context of HMM word-to-phrase alignment. The advantages of our model are that (i) the addition of the syntactic coherence model preserves the efficient parameter estimation procedures; and (ii) the flexibility of the model can be increased so that it can
be tuned according to different objective functions. Experimental results show that tuning this model properly leads to a significant gain in MT performance over the
state-of-the-art
Integrating source-language context into log-linear models of statistical machine translation
The translation features typically used in state-of-the-art statistical machine translation (SMT) model dependencies between the source and target phrases, but not among the phrases in the source language themselves. A swathe of research has demonstrated that integrating source context modelling directly into log-linear phrase-based SMT (PB-SMT) and hierarchical PB-SMT (HPB-SMT), and can positively
influence the weighting and selection of target phrases, and thus improve translation quality. In this thesis we present novel approaches to incorporate source-language contextual modelling into the state-of-the-art SMT models in order to enhance the quality of lexical selection. We investigate the effectiveness of use of a range of contextual features, including lexical features of neighbouring words, part-of-speech tags, supertags, sentence-similarity features, dependency information, and semantic roles. We explored a series of language pairs featuring typologically different languages, and examined the scalability of our research to larger amounts of training data.
While our results are mixed across feature selections, language pairs, and learning curves, we observe that including contextual features of the source sentence
in general produces improvements. The most significant improvements involve the integration of long-distance contextual features, such as dependency relations in
combination with part-of-speech tags in Dutch-to-English subtitle translation, the combination of dependency parse and semantic role information in English-to-Dutch parliamentary debate translation, supertag features in English-to-Chinese translation, or combination of supertag and lexical features in English-to-Dutch subtitle
translation. Furthermore, we investigate the applicability of our lexical contextual model in another closely related NLP problem, namely machine transliteration