Generating qualitative responses has always been a challenge for
human-computer dialogue systems. Existing dialogue systems generally derive
from either retrieval-based or generative-based approaches, both of which have
their own pros and cons. Despite the natural idea of an ensemble model of the
two, existing ensemble methods only focused on leveraging one approach to
enhance another, we argue however that they can be further mutually enhanced
with a proper training strategy. In this paper, we propose ensembleGAN, an
adversarial learning framework for enhancing a retrieval-generation ensemble
model in open-domain conversation scenario. It consists of a
language-model-like generator, a ranker generator, and one ranker
discriminator. Aiming at generating responses that approximate the ground-truth
and receive high ranking scores from the discriminator, the two generators
learn to generate improved highly relevant responses and competitive unobserved
candidates respectively, while the discriminative ranker is trained to identify
true responses from adversarial ones, thus featuring the merits of both
generator counterparts. The experimental results on a large short-text
conversation data demonstrate the effectiveness of the ensembleGAN by the
amelioration on both human and automatic evaluation metrics.

在开放领域对话情景中，提出了一种敌对学习框架 ensembleGAN，用于增强检索 - 生成集合模型，该模型由生成器、排名生成器和排名鉴别器组成，通过学习生成更加相关的响应、高质量候选对抗响应、以及真实响应的识别与区分，实现了两种生成器的优点。实验结果表明了 ensembleGAN 在人工和自动评估度量上的优越性。

EnsembleGAN: 短文本对话检索生成集成模型的敌对学习

EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble  Model on Short-Text Conversation

Finding semantically rich and computer-understandable representations for
textual dialogues, utterances and words is crucial for dialogue systems (or
conversational agents), as their performance mostly depends on understanding
the context of conversations. Recent research aims at finding distributed
vector representations (embeddings) for words, such that semantically similar
words are relatively close within the vector-space. Encoding the "meaning" of
text into vectors is a current trend, and text can range from words, phrases
and documents to actual human-to-human conversations. In recent research
approaches, responses have been generated utilizing a decoder architecture,
given the vector representation of the current conversation. In this paper, the
utilization of embeddings for answer retrieval is explored by using
Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor
(ANN) model, to find similar conversations in a corpus and rank possible
candidates. Experimental results on the well-known Ubuntu Corpus (in English)
and a customer service chat dataset (in Dutch) show that, in combination with a
candidate selection method, retrieval-based approaches outperform generative
ones and reveal promising future research directions towards the usability of
such a system.

本文利用局部敏感哈希树 LSH Forest 实现了基于向量表示的答案检索，并探讨了使用候选项选择方法时，这种方法的优越性。在 Ubuntu 语料库和荷兰的客户服务聊天数据集上进行的实验结果表明，检索式方法优于生成式方法，并揭示了未来研究方向。