Unsupervised Modeling of Twitter Conversations

By Alan Ritter, Colin Cherry, William B. Dolan. 2010

502 citations recorded by the source corpus.

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Citations in the corpus

Towards Exploiting Background Knowledge for Building Conversation Systems (2018, background): “datasets (Lowe et al., 2015b; Ritter et al., 2010; Sordoni et al., 2015) leading to the development of several end-to-end conversation systems (Shang et al.”
A Diversity-Promoting Objective Function for Neural Conversation Models (2015, background): “ersational patterns from data: researchers have begun to explore data-driven generation of conversational responses within the framework of statistical machine translation (SMT), either phrase-based (Ritter et al., 2010), or using neural networks to rerank, or directly in the form of sequence-to-seq”
Ethical Challenges in Data-Driven Dialogue Systems (2017, methodology): “We train two commonly used end-to-end dialogue models, HRED (Serban et al. 2016) and VHRED (Serban et al. 2017c), on the Twitter dataset and sample responses using contexts from the validation set.”
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models (2015, background): “All names and numbers were replaced with the < person > and < number > tokens respectively (Ritter, Cherry, and Dolan 2010).”
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset (2018, background): “Most recent powerful language architectures are trained on vast amounts of barely curated text scrapes, social media conversations, or independent books (Ritter et al., 2010; Zhang et al., 2018; Mazare et al., 2018; Devlin et al., 2018; Liu et al., 2019; Radford et al., 2019).”
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems (2015, background): “Switchboard [7] Human-human Various 2,400 — 3,000,000 Telephone conversations spoken on pre-specified topics DSTC1 [30] Human-computer State 15,000 210,000 Bus ride information spoken tracking system DSTC2 [9] Human-computer State 3,000 24,000 — Restaurant booking spoken tracking system DSTC3 [8] Hu”
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, background): “Recently, several large-scale dialogue datasets have been proposed in order to train data-driven dialogue systems; the Twitter Corpus (Ritter et al., 2010) and the Ubuntu Dialogue corpus (Lowe et al., 2015a) are two examples.”
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling (2018, background): “…two categories: corpora with structured semantic labels (Hemphill et al., 1990; Williams et al., 2013; Asri et al., 2017; Wen et al., 2017; Eric et al., 2017; Shah et al., 2018); and corpora without semantic labels but with an implicit user goal in mind (Ritter et al., 2010; Lowe et al., 2015).”
Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph (2018, background): “While Question Answering (Voorhees and Tice 2000; Wang, Smith, and Mitamura 2007; Yang, Yih, and Meek 2015; Berant et al. 2013; Bordes et al. 2015; Rajpurkar et al. 2016; Nguyen et al. 2016; Onishi et al. 2016; Richardson, Burges, and Renshaw 2013; Berant et al. 2014) and Conversation Systems (Ritte”
Conversational Contextual Cues: The Case of Personalization and History for Response Ranking (2016, background): “Unlike previous efforts that used Twitter as a source of conversations (Ritter et al., 2010), Reddit does not have length constraints, allowing more natural text.”
Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems (2019, background): “, 2017) can leverage large amounts of freely available unannotated data (Ritter et al., 2010; Henderson et al., 2019a).”
Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and Methodology (2020, background): “First, Twitter dialogue sessions are close to spoken natural language and the linguistic styles are close to how people talk in reality [48].”
Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments (2010, background): “…( twitter.com ) is one particularly fruitful source of user-created content, and a ﬂurry of recent research has aimed to understand and exploit these data (Ritter et al., 2010; Shar-iﬁ et al., 2010; Barbosa and Feng, 2010; Asur and Huberman, 2010; O’Connor et al., 2010a; Thelwall et al., 2011).”
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation (2017, background): “Previous work in neural conversation modeling (Ritter et al., 2010; Sordoni et al., 2015) has successfully used Twitter as the source of millions of natural conversations.”
That’s So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets (2015, methodology): “Ritter et al. (2010) study Twitter dialogues using a clustering approach.”
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints (2018, methodology): “Some of the earliest work on data-driven chatbots (Ritter et al., 2011) explored the use of phrase-based Statistical Machine Translation (SMT) on large numbers of conversations gathered from Twitter (Ritter et al., 2010).”
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer (2018, methodology): “The ﬁnal datasets have the following number of instances: Twitter - train [ 58”
Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and Instances (2011, background): “There is growing interest in language analysis for online social media services such as Twitter (Petrovi´c et al., 2010; Ritter et al., 2010), which allows users to broadcast short messages limited to 140 characters.”
Civil Rephrases Of Toxic Texts With Self-Supervised Transformers (2021, background): “The most similar work to ours is Nogueira dos Santos et al. (2018) who trained for the ﬁrst time an encoder-decoder rewriting offensive sentences in a non-offensive register with non-parallel data from Twitter (Ritter et al., 2010) and Reddit (Serban et al., 2017).”