We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE). We present a general strategy to automatically generate one or more sentential hypotheses based on an input sentence and pre-existing manual semantic annotations. The resulting suite of datasets enables us to probe a statistical RTE model’s performance on different aspects of semantics. We demonstrate the value of this approach by investigating the behavior of a popular neural network RTE model.

How should one apply deep learning to tasks such as morphological reinflection, which stochastically edit one string to get another?A recent approach to such sequence-to-sequence tasks is to compress the input string into a vector that is then used to generate the output string, using recurrent neural networks.In contrast, we propose to keep the traditional architecture, which uses a finite-state transducer to score all possible output strings, but to augment the scoring function with the help of recurrent networks.A stack of bidirectional LSTMs reads the input string from leftto-right and right-to-left, in order to summarize the input context in which a transducer arc is applied.We combine these learned features with the transducer to define a probability distribution over aligned output strings, in the form of a weighted finite-state automaton.This reduces hand-engineering of features, allows learned features to examine unbounded context in the input string, and still permits exact inference through dynamic programming.We illustrate our method on the tasks of morphological reinflection and lemmatization.

The narrative cloze is an evaluation metric commonly used for work on automatic script induction.While prior work in this area has focused on count-based methods from distributional semantics, such as pointwise mutual information, we argue that the narrative cloze can be productively reframed as a language modeling task.By training a discriminative language model for this task, we attain improvements of up to 27 percent over prior methods on standard narrative cloze metrics.

Pushpendre Rastogi, Benjamin Van Durme, Raman Arora. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.