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Social Bias Frames: Reasoning about Social and Power Implications of Language
TLDR
It is found that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias, they are not effective at spelling out more detailed explanations in terms of Social Bias Frames. Expand
Automatic Article Commenting: the Task and Dataset
TLDR
A large-scale Chinese dataset with millions of real comments and a human-annotated subset characterizing the comments’ varying quality is introduced, and automatic metrics that generalize a broad set of popular reference-based metrics and exhibit greatly improved correlations with human evaluations are developed. Expand
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
TLDR
This work develops an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator, and achieves state-of-the-art performance on the PDTB benchmark. Expand
A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification
TLDR
A stacking neural network model is proposed to solve the classification problem in which a convolutional neural network is utilized for sentence modeling and a collaborative gated neural network (CGNN) is proposed for feature transformation. Expand
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
TLDR
A new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading is presented, allowing for more focused integration of external knowledge than has been possible in prior approaches. Expand
Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings
TLDR
This paper proposes a neural model utilizing context-aware character-enhanced embeddings to alleviate the drawbacks of the current word level representation and obtains state-of-the-art results. Expand
Counterfactual Story Reasoning and Generation
TLDR
This paper proposes Counterfactual Story Rewriting: given an original story and an intervening counterfactual event, the task is to minimally revise the story to make it compatible with the given counterfactually event. Expand
Probabilistic Graph-based Dependency Parsing with Convolutional Neural Network
TLDR
This paper presents neural probabilistic parsing models which explore up to thirdorder graph-based parsing with maximum likelihood training criteria and evaluated on English and Chinese Penn Treebanks and obtain competitive accuracies. Expand
Shallow Discourse Parsing Using Convolutional Neural Network
TLDR
To improve Non-Explicit sense classification, a Convolutional Neural Network model is proposed to determine the senses for both English and Chinese tasks and a traditional linear model with novel dependency features for Explicit sense classification is explored. Expand
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning
TLDR
This paper proposes DeLorean, a new unsupervised decoding algorithm that can flexibly incorporate both the past and future contexts using only off-the-shelf, left-to-right language models and no supervision. Expand
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