Handling out-of-vocabulary (OOV) words is an important aspect of building encoder-decoder models. OOV words refer to words that are not present in the vocabulary learned during training. There are several approaches to handle OOV words in an encoder-decoder model:

  1. Copy Mechanism: One approach is to use a copy mechanism, which allows the model to directly copy OOV words from the source input to the target output. This can be done by maintaining a dynamic vocabulary that includes both the learned vocabulary and the OOV words encountered during inference.

  2. Subword Units: Another approach is to use subword units, such as Byte-Pair Encoding (BPE) or WordPiece. These methods break down words into smaller subword units, thereby reducing the chances of encountering OOV words. This approach allows the model to handle OOV words through the compositionality of subword units.

  3. Dictionary Extension: In this approach, a predefined external dictionary is used to map OOV words to their closest known counterparts. This dictionary can be built from additional data sources or external resources, such as a pre-trained word embedding model or a large-scale corpus. The model can then be trained to predict the known counterparts using this extended dictionary.

  4. Back-off to Character or Subword Models: If the model encounters an OOV word, it can fall back to a character-level or subword-level model, which has a larger vocabulary and can handle unseen words. This approach involves training separate models on character or subword units to generate the translation for OOV words.

  5. Data Augmentation: Another approach is to augment the training data with artificially generated OOV words. This can be done by randomly replacing some words in the training data with their OOV counterparts, or by introducing synthetic OOV words during the training process. By exposing the model to OOV words during training, it can learn to handle them better during inference.

  It is important to note that the choice of approach depends on the specific requirements and constraints of the task at hand. Some approaches may be more suitable for certain domains or language pairs, while others may require additional computational resources or training data. It is also possible to combine multiple approaches to further improve the handling of OOV words in an encoder-decoder model.