instruction:
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.
question:
We also did a qualitative study on the Starbucks (SBUX) stock movement during this event. Figure FIGREF12 is the daily percentage change of SBUX and NASDAQ index between April 11th and April 20th. SBUX did not follow the upward trend of the whole market before April 17th, and then its change on April 20th, INLINEFORM0 , is quite significant from historical norms. We collected the historical 52 week stock prices prior to this event and calculated the daily stock price change. The distribution of the daily price change of the previous 52 weeks is Figure FIGREF13 with a mean INLINEFORM1 and standard deviation INLINEFORM2 .
Question: How does the method measure the impact of the event on market prices?
answer:
We collected the historical 52 week stock prices prior to this event and calculated the daily stock price change. The distribution of the daily price change of the previous 52 weeks is Figure FIGREF13 with a mean INLINEFORM1 and standard deviation INLINEFORM2 .

question:
Probing Methodology and Modeling ::: Task Definition and Modeling ::: Baselines and Sanity Checks.
When creating synthetic datasets, it is important to ensure that systematic biases, or annotation artifacts BIBREF41, are not introduced into the resulting probes and that the target datasets are sufficiently challenging (or good, in the sense of BIBREF42). To test for this, we use several of the MCQA baseline models first introduced in BIBREF0, which take inspiration from the LSTM-based models used in BIBREF43 for NLI and various partial-input baselines based on these models.
Question: How do they control for annotation artificats?
answer:
we use several of the MCQA baseline models first introduced in BIBREF0

question:
We perform experiments using the following state-of-the-art models: (1) SEQ2SEQ: a sequence-to-sequence model with attention mechanisms BIBREF21, (2) CVAE: a conditional variational auto-encoder model with KL-annealing and a BOW loss BIBREF2, (3) Transformer: an encoder-decoder architecture relying solely on attention mechanisms BIBREF22, (4) HRED: a generalized sequence-to-sequence model with the hierarchical RNN encoder BIBREF23, (5) DialogWAE: a conditional Wasserstein auto-encoder, which models the distribution of data by training a GAN within the latent variable space BIBREF6.
Question: What state of the art models were used in experiments?
answer:
SEQ2SEQ CVAE Transformer HRED DialogWAE