8,737 research outputs found
Interpreting Sentiment Composition with Latent Semantic Tree
As the key to sentiment analysis, sentiment composition considers the
classification of a constituent via classifications of its contained
sub-constituents and rules operated on them. Such compositionality has been
widely studied previously in the form of hierarchical trees including untagged
and sentiment ones, which are intrinsically suboptimal in our view. To address
this, we propose semantic tree, a new tree form capable of interpreting the
sentiment composition in a principled way. Semantic tree is a derivation of a
context-free grammar (CFG) describing the specific composition rules on
difference semantic roles, which is designed carefully following previous
linguistic conclusions. However, semantic tree is a latent variable since there
is no its annotation in regular datasets. Thus, in our method, it is
marginalized out via inside algorithm and learned to optimize the
classification performance. Quantitative and qualitative results demonstrate
that our method not only achieves better or competitive results compared to
baselines in the setting of regular and domain adaptation classification, and
also generates plausible tree explanations.Comment: Findings of ACL202
Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop
The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques
specifically developed for analyzing and understanding the inner-workings and
representations acquired by neural models of language. Approaches included:
systematic manipulation of input to neural networks and investigating the
impact on their performance, testing whether interpretable knowledge can be
decoded from intermediate representations acquired by neural networks,
proposing modifications to neural network architectures to make their knowledge
state or generated output more explainable, and examining the performance of
networks on simplified or formal languages. Here we review a number of
representative studies in each category
Grammar Variational Autoencoder
Deep generative models have been wildly successful at learning coherent
latent representations for continuous data such as video and audio. However,
generative modeling of discrete data such as arithmetic expressions and
molecular structures still poses significant challenges. Crucially,
state-of-the-art methods often produce outputs that are not valid. We make the
key observation that frequently, discrete data can be represented as a parse
tree from a context-free grammar. We propose a variational autoencoder which
encodes and decodes directly to and from these parse trees, ensuring the
generated outputs are always valid. Surprisingly, we show that not only does
our model more often generate valid outputs, it also learns a more coherent
latent space in which nearby points decode to similar discrete outputs. We
demonstrate the effectiveness of our learned models by showing their improved
performance in Bayesian optimization for symbolic regression and molecular
synthesis
From news to comment: Resources and benchmarks for parsing the language of web 2.0
We investigate the problem of parsing the noisy language of social media. We evaluate four all-Street-Journal-trained statistical parsers (Berkeley, Brown, Malt and MST) on a new dataset containing 1,000 phrase structure trees for sentences from microblogs (tweets) and discussion forum posts. We compare the four parsers on their ability to produce Stanford dependencies for these Web 2.0 sentences. We find that the parsers have a particular problem with tweets and that a substantial part of this problem is related to POS tagging accuracy. We attempt three retraining experiments involving Malt, Brown and an in-house Berkeley-style parser and obtain a statistically significant improvement for all three parsers
- …