Exemplar Encoder-Decoder for Neural Conversation Generation

By Gaurav Pandey, Danish Contractor, Vineet Kumar, Sachindra Joshi. 2018

68 citations recorded by the source corpus.

In this paper we present the Exemplar Encoder-Decoder network (EED), a novel conversation model that learns to utilize similar examples from training data to generate responses. Similar conversation examples (context-response pairs) from training data are retrieved using a traditional TF-IDF based retrieval model and the corresponding responses are used by our decoder to generate the ground truth response. The contribution of each retrieved response is weighed by the similarity of corresponding context with the input context. As a result, our model learns to assign higher similarity scores to those retrieved contexts whose responses are crucial for generating the final response. We present detailed experiments on two large data sets and we find that our method out-performs state of the art sequence to sequence generative models on several recently proposed evaluation metrics.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Retrieve and Refine: Improved Sequence Generation Models For Dialogue (2018, methodology): “There is little work in applying these methods to dialogue; one work we are aware of has been done concurrently with ours is Pandey et al. (2018).”
What Makes Good In-Context Examples for GPT-3? (2021, background): “…2018; Guu et al., 2018), QA (Karpukhin et al., 2020; Mao et al., 2020), dialogue generation (Yan et al., 2016; Cai et al., 2018; Song et al., 2016; Pandey et al., 2018; We-ston et al., 2018; Wu et al., 2019), text summarization (Cao et al., 2017; Peng et al., 2019), data-to-text generation (Peng…”
ToTTo: A Controlled Table-To-Text Generation Dataset (2020, methodology): “The technique of editing exemplar sentences has been used in semiparametric generation models (Guu et al., 2018; Pandey et al., 2018; Peng et al., 2019) and crowd-sourcing small, iterative changes to text has been shown to lead to higher-quality data and a more robust annotation process (Little et…”
Challenges in Building Intelligent Open-domain Dialog Systems (2019, methodology): “s a response by jointly attending to query X ⊕C and the topic words. Topic words are also used to model topic transition in multi-turn conversations [146]. The hybrid methods described in Section 2.3 [96, 128, 158] encode the retrieved prototype responses to help generate more informative responses.”
Response Generation by Context-aware Prototype Editing (2018, methodology): “Prior works have attempted to utilize prototype responses to guide the generation process (Song et al. 2016; Pandey et al. 2018), in which prototype responses are encoded into vectors and feed to a de-coder along with a context representation.”
Controllable Paraphrase Generation with a Syntactic Exemplar (2019, methodology): “This dataset allows us to evaluate different approaches quantitatively using standard metrics, including BLEU (Papineni et al., 2002) and ROUGE (Lin, 2004).”
Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation (2019, methodology): “Other approaches have proposed generating more accurate text using semiparametric approaches (Guu et al., 2018; Pandey et al., 2018; Peng et al., 2019), reinforcement learning-based rewards (Paulus et al.”
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory (2018, methodology): “Combination of IR and Generative models To combine IR and generative models, early work (Qiu et al., 2017) tried to re-rank the output from both models. However, the performance of such models is limited by the capacity of individual methods. Most related to our work, Song et al. (2016); Weston et a”
Learning to Represent Edits (2018, background): “, 2019), or implicitly capture it via end-to-end neural networks (Contractor et al., 2018).”
Controllable Semantic Parsing via Retrieval Augmentation (2021, methodology): “Some methods, like CASPER, encode the retrievals alongside the query and let the model decides how to use them (Guu et al., 2018; Hashimoto et al., 2018; He et al., 2020; Weston et al., 2018; Pandey et al., 2018; Lewis et al., 2020).”
Case-based Reasoning for Natural Language Queries over Knowledge Bases (2021, background): “Another recent line of work use training examples at test time to improve language generation (Weston et al., 2018; Pandey et al., 2018; Cao et al., 2018; Peng et al., 2019).”
Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework (2019, background): “Pandey et al., 2018; Wu et al., 2019) or a uni-ﬁed encoder for the concatenation of the query and the retrieved response (Weston et al., 2018).”
Controlling Text Edition by Changing Answers of Specific Questions (2021, background): “Examples are the generation of summaries by template-based rewriting (Cao et al., 2018; Hashimoto et al., 2018) and the generation of text or a response by editing a prototype sentence (Guu et al., 2018; Pandey et al., 2018; Wu et al., 2019).”