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Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e.g., I don't know) regardless of the input. We suggest that the traditional objective function, i.e., the likelihood of output (response) given input (message) is unsuited to response generation tasks. Instead we propose using(More)
Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted , predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led(More)
Natural language generation of coherent long texts like paragraphs or longer documents is a challenging problem for recurrent networks models. In this paper, we explore an important step toward this generation task: training an LSTM (Long-short term memory) auto-encoder to preserve and reconstruct multi-sentence paragraphs. We introduce an LSTM model that(More)
We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A dyadic speaker-addressee model captures properties of interactions between two interlocutors. Our(More)
Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained models of vector-space representations. Yet while 'multi-sense' methods have been proposed and tested on artificial word-similarity tasks, we don't know if they improve real natural language understanding tasks. In this paper we introduce a(More)
Recursive neural models, which use syntactic parse trees to recursively generate representations bottom-up, are a popular architecture. But there have not been rigorous evaluations showing for exactly which tasks this syntax-based method is appropriate. In this paper we benchmark recursive neural models against sequential recurrent neural models (simple(More)
Coherence is what makes a multi-sentence text meaningful, both logically and syntactically. To solve the challenge of ordering a set of sentences into coherent order , existing approaches focus mostly on defining and using sophisticated features to capture the cross-sentence argumenta-tion logic and syntactic relationships. But both argumentation semantics(More)
Implicit discourse relation recognition is a challenging task in the natural language processing field, but important to many applications such as qu estion answering, summarizat ion and so on. Previous research used either art ificially created imp licit discourse relat ions with connectives removed fro m exp licit relations or annotated implicit relat(More)
We propose a simple, fast decoding algorithm that fosters diversity in neural generation. The algorithm modifies the standard beam search algorithm by penalizing hypotheses that are siblings—expansions of the same parent node in the search—thus favoring including hypotheses from diverse parents. We evaluate the model on three neural generation tasks:(More)