Index of papers in Proc. ACL 2009 that mention
  • phrase-based
Galley, Michel and Manning, Christopher D.
Abstract
This paper applies MST parsing to MT, and describes how it can be integrated into a phrase-based decoder to compute dependency language model scores.
Abstract
Our results show that augmenting a state-of-the-art phrase-based system with this dependency language model leads to significant improvements in TER (0.92%) and BLEU (0.45%) scores on five NIST Chinese-English evaluation test sets.
Dependency parsing for machine translation
In this section, we review dependency parsing formulated as a maximum spanning tree problem (McDonald et al., 2005b), which can be solved in quadratic time, and then present its adaptation and novel application to phrase-based decoding.
Dependency parsing for machine translation
We now formalize weighted non-projective dependency parsing similarly to (McDonald et al., 2005b) and then describe a modified and more efficient version that can be integrated into a phrase-based decoder.
Introduction
Hierarchical approaches to machine translation have proven increasingly successful in recent years (Chiang, 2005; Marcu et al., 2006; Shen et al., 2008), and often outperform phrase-based systems (Och and Ney, 2004; Koehn et al., 2003) on.ungetlanguage fluency'and.adequacy; Ilouh ever, their benefits generally come with high computational costs, particularly when chart parsing, such as CKY, is integrated with language models of high orders (Wu, 1996).
Introduction
In comparison, phrase-based decoding can run in linear time if a distortion limit is imposed.
Introduction
Since exact MT decoding is NP complete (Knight, 1999), there is no exact search algorithm for either phrase-based or syntactic MT that runs in polynomial time (unless P = NP).
phrase-based is mentioned in 22 sentences in this paper.
Topics mentioned in this paper:
Kumar, Shankar and Macherey, Wolfgang and Dyer, Chris and Och, Franz
Discussion
We believe that our efficient algorithms will make them more widely applicable in both SCFG—based and phrase-based MT systems.
Experiments
Our phrase-based statistical MT system is similar to the alignment template system described in (Och and Ney, 2004; Tromble et al., 2008).
Experiments
We also train two SCFG—based MT systems: a hierarchical phrase-based SMT (Chiang, 2007) system and a syntax augmented machine translation (SAMT) system using the approach described in Zollmann and Venugopal (2006).
Experiments
Both systems are built on top of our phrase-based systems.
Introduction
These two techniques were originally developed for N -best lists of translation hypotheses and recently extended to translation lattices (Macherey et al., 2008; Tromble et al., 2008) generated by a phrase-based SMT system (Och and Ney, 2004).
Introduction
SMT systems based on synchronous context free grammars (SCFG) (Chiang, 2007; Zollmann and Venugopal, 2006; Galley et al., 2006) have recently been shown to give competitive performance relative to phrase-based SMT.
Translation Hypergraphs
A translation lattice compactly encodes a large number of hypotheses produced by a phrase-based SMT system.
phrase-based is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Liu, Yang and Mi, Haitao and Feng, Yang and Liu, Qun
Background
In phrase-based models, a decision can be translating a source phrase into a target phrase or reordering the target phrases.
Experiments
first model was the hierarchical phrase-based model (Chiang, 2005; Chiang, 2007).
Introduction
We evaluated our joint decoder that integrated a hierarchical phrase-based model (Chiang, 2005; Chiang, 2007) and a tree-to-string model (Liu et al., 2006) on the NIST 2005 Chinese-English test-set.
Introduction
Some researchers prefer to saying “phrase-based approaches” or “phrase-based systems”.
Introduction
On the other hand, other authors (e. g., (Och and Ney, 2004; Koehn et al., 2003; Chiang, 2007)) do use the expression “phrase-based models”.
Joint Decoding
Figure 2(a) demonstrates a translation hypergraph for one model, for example, a hierarchical phrase-based model.
Joint Decoding
Although phrase-based decoders usually produce translations from left to right, they can adopt bottom-up decoding in principle.
Joint Decoding
(2006) propose left-to-right target generation for hierarchical phrase-based translation.
phrase-based is mentioned in 14 sentences in this paper.
Topics mentioned in this paper:
Setiawan, Hendra and Kan, Min Yen and Li, Haizhou and Resnik, Philip
Abstract
Hierarchical phrase-based models are attractive because they provide a consistent framework within which to characterize both local and long-distance reorderings, but they also make it difficult to distinguish many implausible reorderings from those that are linguistically plausible.
Abstract
Rather than appealing to annotation-driven syntactic modeling, we address this problem by observing the influential role of function words in determining syntactic structure, and introducing soft constraints on function word relationships as part of a standard log-linear hierarchical phrase-based model.
Hierarchical Phrase-based System
Formally, a hierarchical phrase-based SMT system is based on a weighted synchronous context free grammar (SCFG) with one type of nonterminal symbol.
Hierarchical Phrase-based System
Synchronous rules in hierarchical phrase-based models take the following form:
Hierarchical Phrase-based System
Translation of a source sentence 6 using hierarchical phrase-based models is formulated as a search for the most probable derivation D* whose source side is equal to e:
Introduction
Hierarchical phrase-based models (Chiang, 2005; Chiang, 2007) offer a number of attractive benefits in statistical machine translation (SMT), while maintaining the strengths of phrase-based systems (Koehn et al., 2003).
Introduction
To model such a reordering, a hierarchical phrase-based system demands no additional parameters, since long and short distance reorderings are modeled identically using synchronous context free grammar (SCFG) rules.
Introduction
Interestingly, hierarchical phrase-based models provide this benefit without making any linguistic commitments beyond the structure of the model.
Overgeneration and Topological Ordering of Function Words
The problem may be less severe in hierarchical phrase-based MT than in BTG, since lexical items on the rules’ right hand sides often limit the span of nonterminals.
phrase-based is mentioned in 19 sentences in this paper.
Topics mentioned in this paper:
Song, Young-In and Lee, Jung-Tae and Rim, Hae-Chang
Abstract
We also present useful features that reflect the compositionality and discriminative power of a phrase and its constituent words for optimizing the weights of phrase use in phrase-based retrieval models.
Introduction
Our approach to phrase-based retrieval is motivated from the following linguistic intuitions: a) phrases have relatively different degrees of significance, and b) the influence of a phrase should be differentiated based on the phrase’s constituents in retrieval models.
Previous Work
One of the most earliest work on phrase-based retrieval was done by (Fagan, 1987).
Previous Work
In many cases, the early researches on phrase-based retrieval have only focused on extracting phrases, not concerning about how to devise a retrieval model that effectively considers both words and phrases in ranking.
Previous Work
While a phrase-based approach selectively incorporated potentially-useful relation between words, the probabilistic approaches force to estimate parameters for all possible combinations of words in text.
Proposed Method
In this section, we present a phrase-based retrieval framework that utilizes both words and phrases effectively in ranking.
Proposed Method
3.1 Basic Phrase-based Retrieval Model
Proposed Method
We start out by presenting a simple phrase-based language modeling retrieval model that assumes uniform contribution of words and phrases.
phrase-based is mentioned in 12 sentences in this paper.
Topics mentioned in this paper:
Xiong, Deyi and Zhang, Min and Aw, Aiti and Li, Haizhou
Abstract
Previous efforts add syntactic constraints to phrase-based translation by directly rewarding/punishing a hypothesis whenever it matches/violates source-side constituents.
Introduction
The phrase-based approach is widely adopted in statistical machine translation (SMT).
Introduction
In such a process, original phrase-based decoding (Koehn et al., 2003) does not take advantage of any linguistic analysis, which, however, is broadly used in rule-based approaches.
Introduction
Since it is not linguistically motivated, original phrase-based decoding might produce ungrammatical or even wrong translations.
The Syntax-Driven Bracketing Model 3.1 The Model
3.3 The Integration of the SDB Model into Phrase-Based SMT
The Syntax-Driven Bracketing Model 3.1 The Model
We integrate the SDB model into phrase-based SMT to help decoder perform syntax-driven phrase translation.
The Syntax-Driven Bracketing Model 3.1 The Model
In this paper, we implement the SDB model in a state-of-the-art phrase-based system which adapts a binary bracketing transduction grammar (BTG) (Wu, 1997) to phrase translation and reordering, described in (Xiong et al., 2006).
phrase-based is mentioned in 14 sentences in this paper.
Topics mentioned in this paper:
Zaslavskiy, Mikhail and Dymetman, Marc and Cancedda, Nicola
Abstract
In this paper, we focus on the reverse mapping, showing that any phrase-based SMT decoding problem can be directly reformulated as a TSP.
Conclusion
The main contribution of this paper has been to propose a transformation for an arbitrary phrase-based SMT decoding instance into a TSP instance.
Introduction
Phrase-based systems (Koehn et al., 2003) are probably the most widespread class of Statistical Machine Translation systems, and arguably one of the most successful.
Introduction
We will see in the next section that some characteristics of beam-search make it a suboptimal choice for phrase-based decoding, and we will propose an alternative.
Introduction
This alternative is based on the observation that phrase-based decoding can be very naturally cast as a Traveling Salesman Problem (TSP), one of the best studied problems in combinatorial optimization.
Phrase-based Decoding as TSP
Successful phrase-based systems typically employ language models of order higher than two.
Related work
In (Tillmann and Ney, 2003) and (Tillmann, 2006), the authors modify a certain Dynamic Programming technique used for TSP for use with an IBM-4 word-based model and a phrase-based model respectively.
Related work
with the mainstream phrase-based SMT models, and therefore making it possible to directly apply existing TSP solvers to SMT.
The Traveling Salesman Problem and its variants
As will be shown in the next section, phrase-based SMT decoding can be directly reformulated as an AGTSP.
phrase-based is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Sun, Jun and Zhang, Min and Tan, Chew Lim
Conclusions and Future Work
The experimental results show that our model outperforms the baseline models and verify the effectiveness of noncontiguous translational equivalences to noncontiguous phrase modeling in both syntax-based and phrase-based systems.
Experiments
We compare the SncTSSG based model against two baseline models: the phrase-based and the STSSG-based models.
Experiments
For the phrase-based model, we use Moses (Koehn et al, 2007) with its default settings; for the STSSG and SncTSSG based models we use our decoder Pisces by setting the following parameters: d = 4, h = 6, C = 6, I: 6, a = 50, ,8 = 50.
Experiments
Table 3 explores the contribution of the noncontiguous translational equivalence to phrase-based models (all the rules in Table 3 has no grammar tags, but a gap <***> is allowed in the last three rows).
Introduction
Current research in statistical machine translation (SMT) mostly settles itself in the domain of either phrase-based or syntax-based.
Introduction
Between them, the phrase-based approach (Marcu and Wong, 2002; Koehn et a1, 2003; Och and Ney, 2004) allows local reordering and contiguous phrase translation.
Introduction
However, it is hard for phrase-based models to learn global reorderings and to deal with noncontiguous phrases.
The Pisces decoder
Consequently, this distortional operation, like phrase-based models, is much more flexible in the order of the target constituents than the traditional syntax-based models which are limited by the syntactic structure.
phrase-based is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Haffari, Gholamreza and Sarkar, Anoop
Abstract
We also provide new highly effective sentence selection methods that improve AL for phrase-based SMT in the multilingual and single language pair setting.
Introduction
0 We introduce new highly effective sentence selection methods that improve phrase-based SMT in the multilingual and single language pair setting.
Sentence Selection: Multiple Language Pairs
For the single language pair setting, (Haffari et al., 2009) presents and compares several sentence selection methods for statistical phrase-based machine translation.
Sentence Selection: Single Language Pair
Phrases are basic units of translation in phrase-based SMT models.
phrase-based is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Liu, Yang and Lü, Yajuan and Liu, Qun
Abstract
Comparable to the state-of-the-art phrase-based system Moses, using packed forests in tree-to-tree translation results in a significant absolute improvement of 3.6 BLEU points over using l-best trees.
Conclusion
Our system also achieves comparable performance with the state-of-the-art phrase-based system Moses.
Experiments
The absence of such non-syntactic mappings prevents tree-based tree-to-tree models from achieving comparable results to phrase-based models.
Related Work
They replace l-best trees with packed forests both in training and decoding and show superior translation quality over the state-of-the-art hierarchical phrase-based system.
phrase-based is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Li, Mu and Duan, Nan and Zhang, Dongdong and Li, Chi-Ho and Zhou, Ming
Collaborative Decoding
Similar to a typical phrase-based decoder (Koehn, 2004), we associate each hypothesis with a coverage vector C to track translated source words in it.
Collaborative Decoding
But to be a general framework, this step is necessary for some state-of-the-art phrase-based decoders (Koehn, 2007; Och and Ney, 2004) because in these decoders, hypotheses with different coverage vectors can coeXist in the same bin, or hypotheses associated with the same coverage vector might appear in different bins.
Experiments
The first one (SYS 1) is re-implementation of Hiero, a hierarchical phrase-based decoder.
phrase-based is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Li, Zhifei and Eisner, Jason and Khudanpur, Sanjeev
Background 2.1 Terminology
In MT, spurious ambiguity occurs both in regular phrase-based systems (e.g., Koehn et al.
Background 2.1 Terminology
Figure l: Segmentation ambiguity in phrase-based MT: two different segmentations lead to the same translation string.
Background 2.1 Terminology
It can be used to encode exponentially many hypotheses generated by a phrase-based MT system (e.g., Koehn et al.
phrase-based is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Lin, Dekang and Wu, Xiaoyun
Introduction
With phrase-based clustering, “Land of Odds” is grouped with many names that are labeled as company names, which is a strong indication that it is a company name as well.
Introduction
The disambiguation power of phrases is also evidenced by the improvements of phrase-based machine translation systems (Koehn et.
Introduction
We demonstrate the advantages of phrase-based clusters over word-based ones with experimental results from two distinct application domains: named entity recognition and query classification.
phrase-based is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Zhang, Hui and Zhang, Min and Li, Haizhou and Aw, Aiti and Tan, Chew Lim
Experiment
We use the first three syntax-based systems (TT2S, TTS2S, FT2S) and Moses (Koehn et al., 2007), the state-of-the-art phrase-based system, as our baseline systems.
Forest-based tree sequence to string model
We use seven basic features that are analogous to the commonly used features in phrase-based systems (Koehn, 2003): l) bidirectional rule mapping probabilities, 2) bidirectional lexical rule translation probabilities, 3) target language model, 4) number of rules used and 5) number of target words.
Related work
Motivated by the fact that non-syntactic phrases make nontrivial contribution to phrase-based SMT, the tree sequence-based translation model is proposed (Liu et al., 2007; Zhang et al., 2008a) that uses tree sequence as the basic translation unit, rather than using single subtree as in the STSG.
phrase-based is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: