instruction:
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
question:
For the task-oriented system, although there are some objective evaluation metrics, such as the number of turns in a dialogue, the ratio of task completion, etc., there is no gold standard for automatically evaluating two (or more) dialogue systems when considering the satisfaction of the human and the fluency of the generated dialogue.
answer:
What problems are found with the evaluation scheme?


question:
The embedding-wise convolution is to apply a convolution filter w $\in \mathbb {R}^{\mathcal {\\}k*d}$ to a window of $k$ word embeddings to generate a new feature, i.e., summarizing a local context of $k$ words. By applying the convolutional filter to all possible windows in the sentence, a feature map $c$ will be generated.
answer:
What supports the claim that injected CNN into recurent units will enhance ability of the model to catch local context and reduce ambiguities?


question:
First, we consider WeedsPrec BIBREF8 which captures the features of INLINEFORM0 which are included in the set of a broader term's features, INLINEFORM1 : DISPLAYFORM0

Second, we consider invCL BIBREF11 which introduces a notion of distributional exclusion by also measuring the degree to which the broader term contains contexts not used by the narrower term. Although most unsupervised distributional approaches are based on the DIH, we also consider the distributional SLQS model based on on an alternative informativeness hypothesis BIBREF10 , BIBREF4 . For completeness, we also include cosine similarity as a baseline in our evaluation.
answer:

Which distributional methods did they consider?