Translation is a complex task that requires more than just knowing two languages. It involves understanding the meaning of words, phrases, and sentences in both languages, as well as how they relate to each other. In this article, we will explore the cognitive aspects of translation, including how readers’ attention is directed when reading and translating text.
The Role of Attention in Translation
Attention plays a crucial role in translation, as it helps readers direct their focus to the most important parts of the text. When reading and translating text, attention is drawn to words, phrases, or sentences that are difficult to understand, ambiguous, or require more context. This process is similar to how our brains process information in our native language, where attention is directed to important details and ignored for less relevant ones.
Hypotheses on Attention in Translation
Based on previous research and cognitive psychology theories, we propose several hypotheses on how attention works during translation:
- Harder-to-translate segments direct more attention to themselves and less to their contexts. This means that when a word or phrase is difficult to translate, the reader’s attention is drawn to it more than to the surrounding context.
- The NMT encoder relies on attention to relevant context to disambiguate input meanings and resolve anaphoric pronouns. When translating text, the neural machine translation (NMT) model uses attention to determine which parts of the input sentence are important for understanding its meaning. This helps the model identify the correct translation for ambiguous words or phrases that rely on context to disambiguate their meaning.
- Low attentional entropy in reading time predicts longer translation times. Attention entropy measures how much attention is directed towards different parts of the input text. When translating text with low attentional entropy, more time is required to understand its meaning and translate it correctly.
- The overall attentional entropy based on Equation 4 can be used to predict translation difficulty. By computing the attentional entropy for a given text, we can estimate how difficult it will be to translate accurately. This can help translators identify texts that are more challenging to translate and require more time and attention.
Conclusion
In conclusion, attention plays a vital role in translation by directing readers’ focus to the most important parts of the text. By understanding how attention works during translation, we can develop more accurate and efficient machine translation models. The hypotheses proposed in this article provide a starting point for further research into the cognitive aspects of translation and can help improve the quality of machine translation systems.
