Neural architecture search (NAS), the task of finding neural architectures automatically, has recently emerged as a promising approach for discovering better models than ones designed by humans alone. However, most success stories are for vision tasks and have been quite limited for text, except for a small language modeling datasets. In this paper, we explore NAS for text sequences at scale, by first focusing on the task of language translation and later extending to reading comprehension. We conduct extensive searches over the recurrent cells and attention similarity functions for standard sequence-to-sequence models across two translation tasks, IWSLT English-Vietnamese and WMT German-English. We report challenges in performing cell searches as well as demonstrate initial success on attention searches with translation improvements over strong baselines. In addition, we show that results on attention searches are transferable to reading comprehension on the SQuAD dataset.
This paper proposes a method to find an effective structure of RNNs and attention mechanisms by searching programs over the stack-oriented execution engine.
Although the new point in this paper looks only the representation paradigm of each program: (possibly variable length) list of the function applications, that could be a flexible framework to find a function without any prior structures like Fig.1-left.
However, the design of the execution engine looks not well-designed. E.g., authors described that the engine ignores the binary operations that could not be executed at the time. But in my thought, such operations should not be included in the set of candidate operations, i.e., the set of candidates should be constrained directly by the state of the stack.
Also, including repeating "identity" operations (in the candidates of attention operations) seems that some unnecessary redundancy is introduced into the search space. The same expressiveness could be achieved by predicting a special token only once at the end of the sequence (namely, "end-of-sequence" token as just same as usual auto-regressive RNN-based decoder models).
Comparison in experiments looks meaningless. Score improvement is slight nevertheless authors paid much computation cost for searching accurate network structures. The conventional method (Zoph&Le,17) in row 3 of Table 1 looks not comparable with proposed methods because it is trained by an out-of-domain task (LM) using conventional (tree-based) search space. Authors should at least show the result by applying the conventional search space to the tasks of this paper.
In Table 2, the "our baseline" looks cheap because the dot product is the least attention model in those proposed in past studies.
The catastrophic score drop in the rows 5 and 7 in Table 1 looks interesting, but the paper does not show enough comprehension about this phenomenon, which makes the proposed method hard to apply other tasks.
The same problem exists in the setting of the hyperparameters in the reward functions. According to the footnote, there are largely different settings about the value of \beta, which suggest a sensitivity by changing this parameter. Authors should provide some criterion to choose these hyperparameters.