Learn Before
Unaligned Data in Sequence Learning
In sequence-to-sequence learning tasks like machine translation, models often must handle unaligned data, where the input and output sequences do not share a strict one-to-one correspondence. Unaligned data introduces two primary challenges: the input and output sequences may have entirely different lengths, and the corresponding regions of meaning may appear in a different order, as seen when translating sentences between languages with different grammatical structures.
0
1
Tags
D2L
Dive into Deep Learning @ D2L
Related
Application of autoregressive generation given a prefix: Machine translation
Statistical Machine Translation vs Neural Machine Translation
Backtranslation
MT Evaluation
MT Corpora
Assessing Translation Effectiveness for a Specific Use Case
A company is developing a translation service for legal documents, where preserving the precise meaning and complex sentence structure of the original text is the highest priority. The company has access to a massive parallel corpus of legal texts. Given these requirements, which approach would be more suitable and why?
Evaluating Machine Translation Quality
Unaligned Data in Sequence Learning