ansonxing23/mt-metrics

Method for Automatic Evaluation of Machine Translation

mt-metrics

Implement four evaluation methods for machine translation. Most open source evaluation programs writed by Python, therefore, I rewrite the methods with Kotlin.

• BLEU (sacrebleu)
• TER (sacrebleu)
• NIST (nltk)
• METEOR (nltk)

Support languages

• EN("en", "English")
• FR("fr", "French")
• DE("de", "German")
• ES("es", "Spanish")
• ZH("zh", "Chinese")
• JA("ja", "Japanese")

How to use

Maven

Make sure you add the dependency below to your pom.xml before building your project.

<dependency>
  <groupId>com.newtranx</groupId>
  <artifactId>mt-metrics</artifactId>
  <version>1.1.6</version>
</dependency>

Usage

Corpus Level

BLEU

val hypothesis = listOf("how are you?", "I'm fine!")
val ref1 = listOf("how are you?", "I'm fine!")
val ref2 = listOf("how do you do?", "I'm ok!")
val references = listOf(ref1, ref2)
val bleu = MetricUtil.buildBleuMetric("en")
val score = bleu.corpusScore(hypothesis, references)

TER

val hypothesis = listOf("how are you?", "I'm fine!")
val ref1 = listOf("how are you?", "I'm fine!")
val ref2 = listOf("how do you do?", "I'm ok!")
val references = listOf(ref1, ref2)
val ter = MetricUtil.buildTerMetric(normalized = true, asianSupport = true)
val score = ter.corpusScore(hypothesis, references)

METEOR

val hypothesis = listOf("how are you?", "I'm fine!")
val ref1 = listOf("how are you?", "I'm fine!")
val ref2 = listOf("how do you do?", "I'm ok!")
val references = listOf(ref1, ref2)

val path = "/home/wordnet"
val wordnet = MetricUtil.buildWordnet(path)
val meteor = MetricUtil.buildMeteorMetric(wordnet, "en")
val score = meteor.corpusScore(hypothesis, references)

NIST

val hypothesis = listOf("how are you?", "I'm fine!")
val ref1 = listOf("how are you?", "I'm fine!")
val ref2 = listOf("how do you do?", "I'm ok!")
val references = listOf(ref1, ref2)
val nist = MetricUtil.buildNistMetric(asianSupport = true)
val score = nist.corpusScore(hypothesis, references)

Sentence Level

BLEU

val hypothesis = "how are you?"
val references = listOf("how are you?", "how do you do?")
val bleu = MetricUtil.buildBleuMetric("en")
val score = bleu.sentenceScore(hypothesis, references)

TER

val hypothesis = "how are you?"
val references = listOf("how are you?", "how do you do?")
val ter = MetricUtil.buildTerMetric(normalized = true, asianSupport = true)
val score = ter.sentenceScore(hypothesis, references)

METEOR

val hypothesis = "how are you?"
val references = listOf("how are you?", "how do you do?")
val language = Language.EN

val path = "/home/wordnet"
val wordnet = MetricUtil.buildWordnet(path)
val meteor = MetricUtil.buildMeteorMetric(wordnet, "en")
val score = meteor.sentenceScore(hypothesis, references)

NIST

val hypothesis = "how are you?"
val references = listOf("how are you?", "how do you do?")
val nist = MetricUtil.buildNistMetric(asianSupport = true)
val score = nist.sentenceScore(hypothesis, references)

API

IEvaluate

sentenceScore(hypothesis: String, references: List): Score

Evaluate hypothesis with multi references.

singleSentenceScore(hypothesis: String, reference: String): Score

Evaluate hypothesis with single reference.

corpusScore(hypotheses: List, references: List<List>): Score

Evaluate whole corpus

MetricUtil.buildBleuMetric(language: String): IEvaluate

Build BLEU metric.

language

Type: String

Must be correct format of Locale string, including ISO code and language in English.

ex. zh Chinese en English

MetricUtil.buildTerMetric(normalized: Boolean, noPunct: Boolean, asianSupport: Boolean, caseSensitive: Boolean): IEvaluate

Build TER metric.

normalized

Type: Boolean

If True, applies basic tokenization to sentences.

noPunct

Type: Boolean

If True, removes punctuations from sentences.

asianSupport

Type: Boolean

If True, adds support for Asian character processing.

caseSensitive

Type: Boolean

If True, does not lowercase sentences.

MetricUtil.buildNistMetric(asianSupport: Boolean, nGram: Int): IEvaluate

Build NIST metric.

nGram

Type: Int

highest n-gram order

asianSupport

Type: Boolean

If True, adds support for Asian character processing.

MetricUtil.buildMeteorMetric(wordnet: IDictionary, language: String, lowercase: Boolean, alpha: Float, beta: Int, gamma: Float): IEvaluate

Build Meteor metric.

wordnet

Type: Boolean

If True, applies basic tokenization to sentences.

language

Type: String

Must be correct format of Locale string, including ISO code and language in English.

ex. zh Chinese en English

lowercase

Type: Boolean

If True, lowercase the input sentence

alpha

Type: Float

parameter for controlling relative weights of precision and recall.

alpha

Type: Float

parameter for controlling relative weights of precision and recall.

beta

Type: Int

parameter for controlling shape of penalty as a function of as a function of fragmentation.

gamma

Type: Float

relative weight assigned to fragmentation penality.