In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

[Q]: As BIBREF0 discussed, the text-only UMT is fundamentally an ill-posed problem, since there are potentially many ways to associate target with source sentences. Intuitively, since the visual content and language are closely related, the image can play the role of a pivot "language" to bridge the two languages without paralleled corpus, making the problem "more well-defined" by reducing the problem to supervised learning.
Question: Why is this work different from text-only UNMT?
[A]: the image can play the role of a pivot "language" to bridge the two languages without paralleled corpus

[Q]: The first part is described as Task A, the purpose of which is to identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set. We chose the best 3 results from each word embeddings group (EE, EP, EC) from Table TABREF19 presenting F1-scores for all models. Then we evaluated these results using more detailed measures for timexes, presented in BIBREF27 .
Question: What experiments are presented?
[A]: identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set Then we evaluated these results using more detailed measures for timexes

[Q]: In addition to the majority baseline, we also compare our results with a lexicon-based approach. For experimental results, we report majority baseline for each language where the majority baseline corresponds to a model's accuracy if it always predicts the majority class in the dataset.
Question: what are the baselines?
[A]:
majority baseline lexicon-based approach