Data-Driven Response Generation in Social Media

By Alan Ritter, Colin Cherry, William B. Dolan. 2011

689 citations recorded by the source corpus.

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Citations in the corpus

CoQA: A Conversational Question Answering Challenge (2018, background): “Moreover, the strong performance of humans indicates that CoQA has a much smaller answer space compared to chit-chat conversations (Ritter et al., 2011; Vinyals and Le, 2015; Sordoni et al., 2015), which supports more reliable automatic evaluation.”
QuAC: Question Answering in Context (2018, background): “19 Dialog ﬁts into an increasing interest in open domain dialog, mostly studied in the context of social chit-chat (Li et al., 2016; Ritter et al., 2011; Fang et al., 2017; Ghazvininejad et al., 2018).”
Generating Sentences by Editing Prototypes (2017, background): “ng machine translation (Kalchbrenner and Blunsom, 2013; Koehn et al., 2007), summarization (Hahn and Mani, 2000; Nallapati et al., 2016), speech recognition (Jurafsky and Martin, 2000), and dialogue (Ritter et al., 2011). State-of-the-art models are largely based on recurrent neural language models ”
A Diversity-Promoting Objective Function for Neural Conversation Models (2015, background): “…together with the * The entirety of this work was conducted at Microsoft. capacity to implicitly learn semantic and syntactic relations between pairs, and to capture contextual dependencies (Sordoni et al., 2015) in a way not possible with conventional SMT approaches (Ritter et al., 2011).”
Does Gender Matter? Towards Fairness in Dialogue Systems (2019, background): “Non-task-oriented dialogue agents, also known as chatbots, are designed to chit-chat with users in open domains for entertainment (Ritter et al., 2011).”
Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators (2019, background): “alternatives, these metrics are still widely used for evaluating response generation (Ritter et al., 2011; Lowe et al., 2017).”
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation (2016, methodology): “These metrics have recently been adopted by dialogue researchers (Ritter et al., 2011; Sordoni et al., 2015; Li et al., 2015; Galley et al., 2015b; Wen et al., 2015; Li et al., 2016).”
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models (2015, background): “Modeling conversations on micro-blogging websites with generative probabilistic models was ﬁrst proposed by Ritter et al. (2011).”
ParlAI: A Dialog Research Software Platform (2017, methodology): “• seq2seq: basic GRU sequence to sequence model (Sutskever et al., 2014) • ir baseline: information retrieval (IR) baseline that scores responses with TFIDF-weighted matching (Ritter et al., 2011).”
Adversarial Learning for Neural Dialogue Generation (2017, background): “, phrase-based machine translation systems (Ritter et al., 2011; Sordoni et al., 2015) or end-to-end neural systems (Shang et al.”
Deep Reinforcement Learning for Dialogue Generation (2016, background): “, 2015) in a way not possible, for example, with MT-based dialogue models (Ritter et al., 2011).”
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses (2015, methodology): “These log-linear models comprise the following feature sets: MT MT features are derived from a large response generation system built along the lines of Ritter et al. (2011) (Ritter et al., 2011).”
Neural Approaches to Conversational AI (2018, positive): “ention mechanism. An instance of this model is CopyNet (Gu et al., 2016), which was shown to signiﬁcantly improve over RNNs thanks to its ability to repeat proper nouns and other words of the input. 4Ritter et al. (2011) also found that alignment produced by an off-the-shelf word aligner (Och and Ne”
A Persona-Based Neural Conversation Model (2016, methodology): “We contrast our baseline against an SMT baseline (Ritter et al., 2011), and the best result (Li et al., 2016) on the established dataset of (Sordoni et al., 2015).”
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues (2016, background): “This is detrimental from two perspectives: from a probabilistic perspective, with stochastic variations injected only at the low level, the model is encouraged to capture local structure in the sequence, rather than global or long-term structure.”
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems (2015, methodology): “Work on unstructured dialogues, recently pi-oneered by Ritter et al. [22], proposed a response generation model for Twitter data based on ideas from Statistical Machine Translation.”
Learning End-to-End Goal-Oriented Dialog (2016, methodology): “This is in sharp contrast to other recent results on data-driven non -goal directed conversations, e.g. over dialogs on Twitter (Ritter et al. , 2011) or Reddit (Dodge et al. , 2016), where it was found that TF-IDF Match outperforms Nearest Neighbor, as general conversations on a given subject typic”
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses (2017, methodology): “We use the Twitter Corpus to train our models as it contains a broad range of non-task-oriented conversations and it has been used to train many state-ofthe-art models.”