Transcript Large Scale Machine Translation Architectures Qin Gao Outline Typical Problems in Machine Translation  Program Model for Machine Translation   MapReduce  Required System Component  Supporting software 

Large Scale Machine Translation
Architectures
Qin Gao
Outline
Typical Problems in Machine Translation
 Program Model for Machine Translation

 MapReduce

Required System Component
 Supporting software
 Distributed streaming data storage system
 Distributed structured data storage system

Integrating – How to make a full-distributed
system
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Why large scale MT
We need more data..


But…
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Some representative MT problems

Counting events in corpora
◦  Ngram count

Sorting
◦  Phrase table extraction

Preprocessing Data
◦ Parsing, tokenizing, etc

Iterative optimization
◦  GIZA++ (All EM algorithms)
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Characteristics of different tasks

Counting events in corpora
◦ Extract knowledge from data

Sorting
◦ Process data, knowledge is inside data

Preprocessing Data
◦ Process data, require external knowledge

Iterative optimization
◦ For each iteration, process data using existing
knowledge and update knowledge
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Components required for large
scale MT
Data
Knowledge
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Components required for large
scale MT
Data
Knowledge
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Components required for large
scale MT
Data
Stream Data
Processor
Structured
Knowledge
Knowledge
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Problem for each component

Stream data:
◦ As the amount of data grows, even a complete
navigation is impossible.

Processor:
◦ Single processor’s computation power is not
enough

Knowledge:
◦ The size of the table is too large to fit into
memory
◦ Cache-based/distributed knowledge base suffers
from low speed
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Make it simple: What is the
underlying problem?

We have a huge cake and we want to cut
them into pieces and eat.

Different cases:
◦ We just need to eat
the cake.
◦ We also want to
count how many
peanuts inside the
cake
◦ (Sometimes)We have
only one folk!
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Parallelization
Data
Knowledge
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Solutions

Large-scale distributed processing
◦ MapReduce: Simplified Data Processing on Large Clusters, Jeffrey Dean, Sanjay
Ghemawat, Communications of the ACM, vol. 51, no. 1 (2008), pp. 107-113.

Handling huge streaming data
◦ The Google File System, Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung,
Proceedings of the 19th ACM Symposium on Operating Systems Principles, 2003, pp.
20-43.

Handling structured data
◦ Large Language Models in Machine Translation, Thorsten Brants, Ashok C. Popat, Peng
Xu, Franz J. Och, Jeffrey Dean, Proceedings of the 2007 Joint Conference on Empirical
Methods in Natural Language Processing and Computational Natural Language
Learning (EMNLP-CoNLL), pp. 858-867.
◦ Bigtable: A Distributed Storage System for Structured Data, Fay Chang, Jeffrey Dean,
Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar
Chandra, Andrew Fikes, Robert E. Gruber, 7th USENIX Symposium on Operating
Systems Design and Implementation (OSDI), 2006, pp. 205-218.
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MapReduce

MapReduce can refer to
◦ A programming model that deal with massive,
unordered, streaming data processing
tasks(MUD)
◦ A set of supporting software environment
implemented by Google Inc

Alternative implementation:
◦ Hadoop by Apache fundation
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MapReduce programming model

Abstracts the computation into two
functions:
◦ MAP
◦ Reduce

User is responsible for the
implementation of the Map and Reduce
functions, and supporting software take
care of executing them
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Representation of data
The streaming data is abstracted as a
sequence of key/value pairs
 Example:

◦ (sentence_id : sentence_content)
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Map function

The Map function takes an input
key/value pair, and output a set of
intermediate key/value pairs
Key1 : Value1
Key1 : Value1
Key2 : Value2
Key3 : Value3
Map()
……..
Key2 : Value2
Key1 : Value2
Key2 : Value1
Key3 : Value3
Map()
……..
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Reduce function

Reduce function accepts one intermediate
key and a set of intermediate values, and
produce the result
Key1 : Value1
Key1 : Value2
Key1 : Value3
Reduce()
Result
Reduce()
Result
……..
Key2 : Value1
Key2 : Value2
Key2 : Value3
……..
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The architecture of MapReduce
Map
function
Reduce
Function
Distributed
Sort
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Benefit of MapReduce
Automatic splitting data
 Fault tolerance
 High-throughput computing, uses the
nodes efficiently
 Most important: Simplicity, just need to
convert your algorithm to the MapReduce
model.

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Requirement for expressing
algorithm in MapReduce

Process Unordered data
◦ The data must be unordered, which means no
matter in what order the data is processed,
the result should be the same

Produce Independent intermediate key
◦ Reduce function can not see the value of
other keys
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Example

Distributed Word Count (1)
◦
◦
◦
◦
◦

Input key : word
Input value : 1
Intermediate key : constant
Intermediate value: 1
Reduce() : Count all intermediate values
Distributed Word Count (2)
◦
◦
◦
◦
Input key : Document/Sentence ID
Input value : Document/Sentence content
Intermediate key : constant
Intermediate value: number of words in the
document/sentence
◦ Reduce() : Count all intermediate values
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Example 2

Distributed unigram count
◦
◦
◦
◦
Input key : Document/Sentence ID
Input value : Document/Sentence content
Intermediate key : Word
Intermediate value: Number of the word in the
document/sentence
◦ Reduce() : Count all intermediate values
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Example 3

Distributed Sort
◦ Input key : Entry key
◦ Input value : Entry content
◦ Intermediate key : Entry key (modification
may be needed for ascend/descend order)
◦ Intermediate value: Entry content
◦ Reduce() : All the entry content

Making use of built-in sorting functionality
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Supporting MapReduce: Distributed
Storage

Reminder what we are dealing with in
MapReduce:
◦ Massive, unordered, streaming data

Motivation:
◦ We need to store large amount of data
◦ Make use of storage in all the nodes
◦ Automatic replication
 Fault tolerant
 Avoid hot spots client can read from many servers

Google FS and Hadoop FS (HDFS)
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Design principle of Google FS

Optimizing for special workload:
◦ Large streaming reads, small random reads
◦ Large streaming writes, rare modification

Support concurrent appending
◦ It actually assumes data are unordered
High sustained bandwidth is more
important than low latency, fast response
time is not important
 Fault tolerant

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Google FS Architecture
Optimize for large streaming reading and
large, concurrent writing
 Small random reading/writing is also
supported, but not optimized
 Allow appending to existing files
 File are spitted into chunks and stored in
several chunk servers
 A master is responsible for storage and
query of chunk information

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Google FS architecture
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Replication
When a chunk is frequently or
“simultaneously” read from a client, the
client may fail
 A fault in one client may cause the file not
usable
 Solution: store the chunks in multiple
machines.
 The number of replica of each chunk :
replication factor

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HDFS
HDFS shares similar design principle of
Google FS
 Write-once-read-many : Can only write
file once, even appending is now allowed
 “Moving computation is cheaper than
moving data”

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Are we done?
NO… Problems about the existing architecture
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We are good at dealing with data
What about knowledge? I.E. structured
data?
 What if the size of the knowledge is
HUGE?

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A good example: GIZA

A typical EM algorithm
World
Alignment
Collect
Counts
Has More
Sentences?
Y
Has More
Iterations
?
Y
N
Normalize
Counts
N
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When parallelized: seems to be a
perfect MapReduce application
Word
Alignment
Word
Alignment
Word
Alignment
Collect
Counts
Collect
Counts
Collect
Counts
Has More
Sentences?
N
Y
Has More
Iterations?
Y
Has More
Sentences?
Y
N
Has More
Sentences?
Y
N
Normalize
Counts
N
Run on cluster
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However:
Memory
Large parallel corpus
…
Corpus
chunks
Map
Reduce
Count
tables
. ... ..
. .
.... ..
. ... ..
. .
.... ..
. ... ..
. .
.... ..
. ... .. . .
. . .
.... .. . .
. .. . .
Combined
count table
. ... ..
. .
.... ..
Data I/O
Memory
Renormalization
Statistical
lexicon
Redistribute for
next iteration
. ... .. . .
. . .
.... .. . .
. .. . .
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Huge tables
Lexicon probability table: T-Table
 Up to 3G in early stages
 As the number of workers increases, they
all need to load this 3G file!
 And all the nodes need to have 3G+
memory – we need a cluster of super
computers?

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Another example, decoding
Consider language models, what can we
do if the language model grows to several
TBs
 We need storage/query mechanism for
large, structured data
 Consideration:

◦ Distributed storage
◦ Fast access: network has high latency
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Google Language Model

Storage:
◦ Central storage or distributed storage

How to deal with latency?
◦ Modify the decoder, collect a number of
queries and send them in one time.

It is a specific application, we still need
something more general.
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Again, made in Google:
Bigtable
It is the specially optimized for structured
data
 Serving many applications now
 It is not a complete database
 Definition:

◦ A Bigtable is a sparse, distributed, persistent,
multi-dimensional, sorted map
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Data model in Bigtable

Four dimension table:
◦
◦
◦
◦
Row
Column family
Column
Timestamp
Column family
Column
Row
Timestamp
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Distributed storage unit : Tablet
A tablet consists a range of rows
 Tablets can be stored in different nodes,
and served by different servers
 Concurrent reading multiple rows can be
fast

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Random access unit : Column family
Each tablet is a string-to-string map
 (Though not mentioned, the API shows
that: ) In the level of column family, the
index is loaded into memory so fast
random access is possible
 Column family should be fixed

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Tables inside table: Column and
Timestamp
Column can be any arbitrary string value
 Timestamp is an integer
 Value is byte array
 Actually it is a table of tables

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Performance

Number of 1000-byte values read/write per second.

What is shocking:
◦ Effective IO for random read (from GFS) is more than 100
MB/second
◦ Effective IO for random read from memory is
more than 3 GB/second
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An example : Phrase Table
Row: First bigram/trigram of the source
phrase
 Column Family: Length of source phrase
or some hashed number of remaining
part of source phrase
 Column: Remaining part of the source
phrase
 Value: All the phrase pairs of the source
phrase

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Benefit
Different source phrase comes from
different servers
 The load is balanced and the reading can
be concurrent and much faster.
 Filtering the phrase table before decoding
becomes much more efficient.

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Another Example: GIZA++

Lexicon table:
◦
◦
◦
◦

Row: Source word id
Column Family: nothing
Column: Target word id
Value: The probability value
With a simple local cache, the table
loading can be extremely efficient
comparing to current implemenetation
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Conclusion
Strangely, the talk is all about how Google
does it
 A useful framework for distributed MT
systems require three components:

◦ MapReduce software
◦ Distributed streaming data storage system
◦ Distributed structured data storage system
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Open Source Alternatives
MapReduce Library  Hadoop
 GoogleFS  Hadoop FS (HDFS)
 BigTable  HyperTable

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THANK YOU!
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