What is machine translation?
Machine translation is the automatic translation of text or speech from one natural language to another by a computer system.
Why is machine translation an important topic in computer-supported interaction?
It enables communication across language barriers, supporting global interaction and information access.
What is the main goal of machine translation systems?
To produce a target-language sentence that preserves the meaning of the source-language sentence as accurately and naturally as possible.
Why is machine translation considered a difficult problem?
Natural languages are ambiguous, context-dependent, and structurally different, making direct translation challenging.
What kinds of ambiguities affect machine translation?
Lexical ambiguity (word meaning), syntactic ambiguity (sentence structure), and semantic ambiguity (intended meaning).
Why can literal word-by-word translation fail?
Different languages use different grammar, word order, idioms, and expressions that cannot be translated directly.
What role does context play in machine translation?
Context helps determine the correct meaning of words and phrases that have multiple possible translations.
How does machine translation relate to natural language processing (NLP)?
Machine translation is a core application of NLP that combines syntax, semantics, and statistical or learned language patterns.
What is a source language in machine translation?
The source language is the original language of the text or speech to be translated.
What is a target language in machine translation?
The target language is the language into which the text or speech is translated.
Why is machine translation a machine learning problem?
Translation patterns are learned from large bilingual datasets rather than being fully hand-coded.
What is parallel data in machine translation?
Parallel data consists of sentence pairs that are translations of each other in two different languages.
Why is large-scale data important for machine translation?
More data allows models to learn better translation patterns and handle rare words or structures.
What is statistical machine translation (SMT)?
SMT uses statistical models learned from bilingual corpora to choose the most probable translation.
What is neural machine translation (NMT)?
NMT uses neural networks to model translation as a single end-to-end learning problem.
Why has neural machine translation largely replaced earlier approaches?
It produces more fluent and context-aware translations by modeling entire sentences holistically.
What does “end-to-end” mean in neural machine translation?
The system learns to translate directly from source text to target text without separate hand-designed components.
How is machine translation evaluated?
Evaluation compares machine-generated translations to reference translations using automatic metrics or human judgment.
Why is evaluation difficult in machine translation?
There can be multiple correct translations for the same sentence.
What is an example of a real-world application of machine translation?
Online translation services, multilingual chat systems, and cross-language information retrieval.
How does machine translation support multilingual interaction?
It allows users speaking different languages to communicate without learning each other’s languages.
What are common sources of translation errors?
Ambiguous words, idiomatic expressions, missing context, and insufficient training data.
Why is machine translation still an open research problem?
Perfect translation requires deep understanding of language, culture, and context, which machines still struggle with.
How does machine translation connect to speech recognition and multimodal interaction?
Spoken language can be recognized, translated, and presented using multiple modalities such as speech and text.
What is the overall aim of machine translation research?
To enable accurate, fluent, and context-aware translation that supports natural human communication.
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