TASK DEFINITION: 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.
PROBLEM: Suppose there are $M$ total layers, and define a subset of these layers at which to apply the loss function: $K = \lbrace k_1, k_2, ..., k_L
brace \subseteq \lbrace 1,..,M-1
brace $. The total objective function is then defined as

where $Z_{k_l}$ is the $k_l$-th Transformer layer activations, $Y$ is the ground-truth transcription for CTC and context dependent states for hybrid ASR, and $Loss(P, Y)$ can be defined as CTC objective BIBREF11 or cross entropy for hybrid ASR. T

SOLUTION: Do they just sum up all the loses the calculate to end up with one single loss?

PROBLEM: 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.

SOLUTION: What state of the art models were used in experiments?

PROBLEM: However, recent work has found that many NLI datasets contain biases, or annotation artifacts, i.e., features present in hypotheses that enable models to perform surprisingly well using only the hypothesis, without learning the relationship between two texts BIBREF2 , BIBREF3 , BIBREF4 . For instance, in some datasets, negation words like "not" and "nobody" are often associated with a relationship of contradiction. As a ramification of such biases, models may not generalize well to other datasets that contain different or no such biases.

SOLUTION:
Is such bias caused by bad annotation?