Generalized templates for EBMT

Speaker: Rashmi Gangadharaiah
Location: GHC 6501

Topic: Generalized templates for EBMT

Abstract:
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Example-Based Machine Translation (EBMT), like other corpus based methods, requires substantial parallel training data. One way to reduce data requirements and improve translation quality is to generalize parts of the parallel corpus into translation templates. This automated generalization process requires clustering. In most clustering approaches the optimal number of clusters (N) is found empirically on a development set which often takes several days. We introduce a spectral clustering framework that automatically estimates the optimal N and removes unstable oscillating points. The new framework produces significant improvements in low-resource EBMT settings for English-to-French (~1.4 BLEU points), English-to-Chinese (~1 BLEU point), and English-to-Haitian (~2 BLEU points). The translation quality with templates created using automatically and empirically found best N were almost the same. By discarding “incoherent” points, a further boost in translation scores is observed, even above the empirically found N.

Two talks

(1) Greg Hanneman:
Title: The Stat-XFER Group Submission for WMT '10

Abstract:
Each year, the Workshop in Statistical Machine Translation collects state-of-the-art MT results for a variety of European language pairs via a shared translation task. In this talk, I will describe the CMU's Stat-XFER MT group submission to this year's WMT French--English track, our third submission to the WMT series, using the Joshua decoder. A large focus will be on new modeling decisions or system-building techniques that have changed from eariler submissions based on new research carried out in our group. I will also present some open questions facing builders of large-scale hierarchcial MT systems in general.


(2) Vamshi Ambati:
Title: Making sense of Crowd data for Machine Translation

Abstract:
Quality of crowd data is a common concern in crowd-sourcing approaches to data collection. When working with crowd data, the objectives are two-fold - maximizing the quality of data from non-experts, and minimizing the cost of annotation by pruning noisy annotators.
I will discuss our recent experiments in Machine Translation for selection of high quality crowd translations by explicitly modeling annotator reliability based on agreement with other submissions. I will also present some preliminary results in cost minimization and report their adaptation and feasibility to machine translation.

Nonparametric Word Segmentation for Machine Translation

Speaker: Thuylinh Nguyen
Title: Nonparametric Word Segmentation for Machine Translation
Thursday 18 Feb 2010. 12-1:30pm in GHC 4405.

In this talk we present an unsupervised word segmentation for machine
translation. The model utilizes existing nonparametric monolingual
segmentations. The monolingual segmentation model and the bilingual word
alignment model are coupled so that source text segmentation optimizes
the one-to-one mapping with the target text. Often, there are words in
the source language that do not appear in target language and vise
versa. Our model therefore models source language word deletion and word
insertion. The experiments show improvements on Arabic-English and
Chinese-English translation tasks.

LoonyBin: Making Empirical MT Reproducible, Efficient, and Less Annoying

Speaker: Jonathan Clark
When: Tuesday, January 19 at Noon
Where: GHC 6501
What: Free Knowledge and Free Food
Title: LoonyBin: Making Empirical MT Reproducible, Efficient, and
Less Annoying

Abstract: Construction of machine translation systems has evolved into
a multi-stage workflow involving many complicated dependencies. Many
decoder distributions have addressed this by including monolithic
training scripts – train-factored-model.pl for Moses and mr_runmer.pl
for SAMT. However, such scripts can be tricky to modify for novel
experiments and typically have limited support for the variety of job
schedulers found on academic and commercial computer clusters. Further
complicating these systems are hyperparameters, which often cannot be
directly optimized by conventional methods requiring users to
determine which combination of values is best via trial and error. The
recently-released LoonyBin open-source workflow management tool
addresses these issues by providing: 1) a visual interface for the
user to create and modify workflows; 2) a well-defined logging
mechanism; 3) a script generator that compiles visual workflows into
shell scripts, and 4) the concept of Hyperworkflows, which intuitively
and succinctly encodes small experimental variations within a larger
workflow. We also describe the Machine Translation Toolpack for
LoonyBin, which exposes state-of-the-art machine translation tools as
drag-and-drop components within LoonyBin.

MEMT and METEOR

Kenneth Heafield and Michael Denkowski: Features for System Combination
(This is work done as an MT lab project.)

Michael will give an update on his recent work on the METEOR MT evaluation matrix.

10 Dec 2009, Thursday, 12:00-1:30, in GHC 6501

Lori's talk

Speaker: Lori Levin
Where: GHC 6501
When: Nov 09, 2009 - Tuesday - Noon
Title: A Pendulum Swung Too Far
Abstract: This paper by Ken Church deals with the never ending battle between Empiricism and Rationalism,
esp. its incarnation in NLP. Lori will summarize and present the arguments formulated in the
paper. She will then continue with her own views on why linguistics
needs to be brought back into NLP and MT in particular.

Two talks

Talk 1:
Nguyen Bach: Source-side Dependency Tree Reordering Models with Subtree Movements and Constraints

Abstract: We propose a novel source-side dependency tree reordering model for statistical machine translation, in which subtree movements and constraints are represented as reordering events associated with the widely used lexicalized reordering models. This model allows us to not only efficiently capture the statistical distribution of the subtree-to-subtree transitions in training data, but also utilize it directly at the decoding time to guide the search process. Using subtree movements and constraints as features in a log-linear model, we are able to help the reordering models make better selections. It also allows the subtle importance of monolingual syntactic movements to be learned alongside other reordering features. We show improvements in translation quality in English-Spanish and English-Iraqi translation tasks.

This is joint work with Qin Gao and Stephan Vogel.

Talk 2:
Francisco (Paco) Guzman: Reassessment of the Role of Phrase Extraction in SMT

Abstract: In this paper we study in detail the relation between word alignment and phrase extraction. First, we analyze different word alignments according to several characteristics and compare them to hand-aligned data. Then, we analyze the phrase-pairs generated by these alignments. We observed that the number of unaligned words has a large impact on the characteristics of the phrase table. A manual evaluation of phrase pair quality showed that the increase in the number of unaligned words results in a lower quality. Finally, we present translation results from using the number of unaligned words as features from which we obtain up to 2BP of improvement.

This is joint work with Qin Gao and Stephan Vogel.