Assessing Incrementality in Sequence-to-Sequence Models

Abstract

Since their inception, encoder-decoder models have successfully been applied to a wide array of problems in computational linguistics. The most recent successes are predominantly due to the use of different variations of attention mechanisms, but their cognitive plausibility is questionable. In particular, because past representations can be revisited at any point in time, attention-centric methods seem to lack an incentive to build up incrementally more informative representations of incoming sentences. This way of processing stands in stark contrast with the way in which humans are believed to process language: continuously and rapidly integrating new information as it is encountered. In this work, we propose three novel metrics to assess the behavior of RNNs with and without an attention mechanism and identify key differences in the way the different model types process sentences.

Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental NLU

“…Hupkes et al (2018) use a diagnostic classifier to analyze the representations that are incrementally built by sequence-to-sequence models in disfluency detection and conclude that the semantic information is only kept encoded for a few steps after it appears in the dialogue, being soon forgotten afterwards. Ulmer et al (2019) propose three metrics to assess the incremental encoding abilities of LSTMs and compare it with the addition of attention mechanisms.…”