35 research outputs found
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another neural network with access to connectives, and thus encouraged to
extract similarly salient features for accurate classification. We develop an
adversarial model to enable an adaptive imitation scheme through competition
between the implicit network and a rival feature discriminator. Our method
effectively transfers discriminability of connectives to the implicit features,
and achieves state-of-the-art performance on the PDTB benchmark.Comment: To appear in ACL201
Automatic Article Commenting: the Task and Dataset
Comments of online articles provide extended views and improve user
engagement. Automatically making comments thus become a valuable functionality
for online forums, intelligent chatbots, etc. This paper proposes the new task
of automatic article commenting, and introduces a large-scale Chinese dataset
with millions of real comments and a human-annotated subset characterizing the
comments' varying quality. Incorporating the human bias of comment quality, we
further develop automatic metrics that generalize a broad set of popular
reference-based metrics and exhibit greatly improved correlations with human
evaluations.Comment: ACL2018; with supplements; Dataset link available in the pape
Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts
Fine-tuning continuous prompts for target tasks has recently emerged as a
compact alternative to full model fine-tuning. Motivated by these promising
results, we investigate the feasibility of extracting a discrete (textual)
interpretation of continuous prompts that is faithful to the problem they
solve. In practice, we observe a "wayward" behavior between the task solved by
continuous prompts and their nearest neighbor discrete projections: We can find
continuous prompts that solve a task while being projected to an arbitrary text
(e.g., definition of a different or even a contradictory task), while being
within a very small (2%) margin of the best continuous prompt of the same size
for the task. We provide intuitions behind this odd and surprising behavior, as
well as extensive empirical analyses quantifying the effect of various
parameters. For instance, for larger model sizes we observe higher waywardness,
i.e, we can find prompts that more closely map to any arbitrary text with a
smaller drop in accuracy. These findings have important implications relating
to the difficulty of faithfully interpreting continuous prompts and their
generalization across models and tasks, providing guidance for future progress
in prompting language models.Comment: NAACL 202