Abstract: The ability to reason with natural language is a fundamental prerequisite for many NLP tasks such as information extraction, machine translation and question answering. To quantify this ability, systems are commonly tested whether they can recognize textual entailment, i.e., whether one sentence can be inferred from another one. However, in most NLP applications only single source sentences instead of sentence pairs are available. Hence, we propose a new task that measures how well a model can generate an entailed sentence from a source sentence. We take entailment-pairs of the Stanford Natural Language Inference corpus and train an LSTM with attention. On a manually annotated test set we found that 82% of generated sentences are correct, an improvement of 10.3% over an LSTM baseline. A qualitative analysis shows that this model is not only capable of shortening input sentences, but also inferring new statements via paraphrasing and phrase entailment. We then apply this model recursively to input-output pairs, thereby generating natural language inference chains that can be used to automatically construct an entailment graph from source sentences. Finally, by swapping source and target sentences we can also train a model that given an input sentence invents additional information to generate a new sentence.

Introduction: The ability to determine entailment or contradiction between natural language text is essential for improving the performance in a wide range of natural language processing tasks. Recognizing Textual Entailment (RTE) is a task primarily designed to determine whether two natural language sentences are independent, contradictory or in an entailment relationship where the second sentence (the hypothesis) can be inferred from the first (the premise). Although systems that perform well in RTE could potentially be used to improve question answering, information extraction, text summarization and machine translation BIBREF0 , only in few of such downstream NLP tasks sentence-pairs are actually available. Usually, only a single source sentence (e.g. a question that needs to be answered or a source sentence that we want to translate) is present and models need to come up with their own hypotheses and commonsense knowledge inferences. The release of the large Stanford Natural Language Inference (SNLI) corpus BIBREF1 allowed end-to-end differentiable neural networks to outperform feature-based classifiers on the RTE task BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . In this work, we go a step further and investigate how well recurrent neural networks can produce true hypotheses given a source sentence. Furthermore, we qualitatively demonstrate that by only training on input-output pairs and recursively generating entailed sentence we can generate natural language inference chains (see Figure 1 for an example). Note that every inference step is interpretable as it is mapping one natural language sentence to another one. Our contributions are fourfold: (i) we propose an entailment generation task based on the SNLI corpus (§ "Entailment Generation" ), (ii) we investigate a sequence-to-sequence model and find that $82\%$ of generated sentences are correct (§ "Example Generations" ), (iii) we demonstrate the ability to generate natural language inference chains trained solely from entailment pairs (§ "Entailment Generation"1 ), and finally (iv) we can also generate sentences with more specific information by swapping source and target sentences during training (§ "Entailment Generation"6 ).

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Answer: The health benefits of alcohol consumption are more limited than previously thought, researchers say