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Generative Deep Neural Networks for Dialogue Presented By Shantanu Kumar Adapted from slides by Iulian Vlad Serban What are Dialogue Systems? Computer system that can converse like a human with another human while making sense

  1. Generative Deep Neural Networks for Dialogue Presented By Shantanu Kumar Adapted from slides by Iulian Vlad Serban

  2. What are Dialogue Systems? • Computer system that can converse like a human with another human while making sense • Types of Dialogue • Open Domain • Task Oriented

  3. Applications of Dialogue Systems • Technical Support • Product enquiry • Website navigation • HR helpdesk • Error diagnosis • IVR system in Call Centres • Entertainment • IoT interface • Virtual Assistants • Siri, Cortana, Google Assistant • Assistive technology • Simulate human conversations

  4. How do we build such a system??

  5. Traditional Pipeline models

  6. End-To-End models with DL Dialogue Context Neural Network Response

  7. End-To-End models with DL Knowledge Database Actions

  8. What is a good Chatbot? The responses should be • Grammatical • Coherent • In Context • Ideally non-Generic responses

  9. How can we learn the model? - Unsupervised Learning (Generative Models) - Maximise likelihood w.r.t. words - Supervised Learning Maximise likelihood w.r.t. annotated labels - - Reinforcement Learning - Learning from real users - Learning from simulated users - Learning with given reward function

  10. Generative Dialogue Modeling Decomposing Dialogue Probability, Decomposing Utterance Probability,

  11. Generative Dialogue Modeling Maximising likelihood on fixed corpora - Imitating human dialogues

  12. Generative Dialogue Modeling Models proposed with three inductive biases • Long-term memory - Recurrent units used (GRU) • High-level compositional structure - Hierarchical structure - Multi resolution representation (MRRNN paper) • Representing uncertainty and ambiguity - Latent variables (MRRNN and VHRED)

  13. Hierarchical Recurrent Encoder-Decoder (HRED) Akshay: - Encoder RNN Can be applied to - For encoding each utterance independently into arbitrary an utterance vector lengths - Context RNN - For encoding the topic/context of the dialogue up till the current utterance using utterance vectors - Decoder RNN - For predicting the next utterance

  14. Hierarchical Recurrent Encoder-Decoder (HRED)

  15. Hierarchical Recurrent Encoder-Decoder (HRED) Bidirectional HRED - Encoder RNN -> Bidirectional - Forward and Backward RNNs combined to get fixed length representation - Concat last state of each RNN - Concat of L2 pooling over temporal dimension

  16. Hierarchical Recurrent Encoder-Decoder (HRED) Bootstrapping Barun Akshay Prachi - Initialising with Word2Vec embeddings Dinesh - Trained on Google News dataset Gagan - Pre-training on SubTle Q-A dataset - 5.5M Q-A pairs Prachi: 2 - Converted to 2-turn dialogue stage D = {U1 = Q, U2 = A} training

  17. Gagan, Dataset - MovieTriples dataset Rishabh, Dinesh Why only triples? Anshul: Split train/ • Open Domain - Wide variety of topics covered val on • Names and Numbers replaced with <person> and <number> tokens movies? • Vocab of 10K most popular tokens • Special <continued-utterance> and <end-of-utterance> tokens to capture breaks

  18. Dialogue Modeling Ubuntu Dialog Corpus - Goal-driven: Users resolve technical problems - ~0.5M dialogues Twitter Dialog Corpus - Open-domain: Social chit-chat - ~0.75M dialogues in Train, 100K for Val and Test - 6.27 utterance and 94 tokens per dialogue

  19. Example - Ubuntu Corpus User Expert Hello! Recently I updated to ubuntu 12.04 LTS and I am unsatisfied by its performance. I am facing a bug since the upgrade to 12.04 LTS. Can anyone help??????????

  20. Example - Ubuntu Corpus User Expert Hello! Recently I updated to ubuntu 12.04 LTS and I am unsatisfied by its performance. I am facing a bug since the upgrade to 12.04 LTS. Can anyone help?????????? You need to give more details on the issue.

  21. Example - Ubuntu Corpus User Expert Hello! Recently I updated to ubuntu 12.04 LTS and I am unsatisfied by its performance. I am facing a bug since the upgrade to 12.04 LTS. Can anyone help?????????? You need to give more details on the issue. Every time I login it gives me "System Error" pop up. It is happing since I upgraded to 12.04.

  22. Example - Ubuntu Corpus User Expert Hello! Recently I updated to ubuntu 12.04 LTS and I am unsatisfied by its performance. I am facing a bug since the upgrade to 12.04 LTS. Can anyone help?????????? You need to give more details on the issue. Every time I login it gives me "System Error" pop up. It is happing since I upgraded to 12.04. Send a report, or cancel it.

  23. Example - Ubuntu Corpus User Expert Hello! Recently I updated to ubuntu 12.04 LTS and I am unsatisfied by its performance. I am facing a bug since the upgrade to 12.04 LTS. Can anyone help?????????? You need to give more details on the issue. Every time I login it gives me "System Error" pop up. It is happing since I upgraded to 12.04. Send a report, or cancel it. I have already done that but after few min, it pops up again...

  24. Example - Twitter Corpus Person A Person B Hanging out in the library for the past couple hours makes me feel like I'll do great on this test! @smilegirl400 wow, what a nerd lol jk haha =p what!? you changed your bio =( @smileman400 Do you like my bio now? I feel bad for changing it but I like change. =P @smilegirl400 yes I do =) It definitely sums up who you are lisa. Yay! you still got me =)

  25. Evaluation Metric Barun Akshay Dinesh - Word Perplexity Rishabh - Measures the probability of generating the exact Arindam reference utterance Anshul - Word error-rate - Number of words in the dataset the model has predicted incorrectly divided by the total number of words in the dataset. - Penalises diversity [Akshay]

  26. Evaluation Metric - Word Perplexity - Can only be used with generative models - Given an utterance, what is the probability? How do we evaluate given an output utterance? - Multi-modal output - Space of possible valid utterance is huge - Human annotation is expensive and slow

  27. Evaluation Metric How do we evaluate given an output utterance? - Multi-modal output - Space of possible valid utterance is huge - Human annotation is expensive and slow Automatic Evaluation Metrics - Word overlap measure (BLEU, ROUGE, Levenshtein dist.) - Embedding based measures - Poor correlation with Human annotation

  28. Results Lack of error analysis

  29. MAP Output Nupur: MAP vs Stochastic Sampling - Most probable last utterance - Found using beam search for better approximation - Generic responses observed - Stochastic sampling gives more diverse dialogues

  31. Extensions Model • [Barun][Rishabh] Attention model during decoding for long contexts • [Prachi] Dialogue systems with multiple participants • Different decoders for each participant? • Order of speaking • [Rishabh] Incorporating outside knowledge using KB

  32. Extensions Data • [Akshay][Surag] Use bigger datasets like Reddit for dialogue • [Rishabh] Using film dialogue scripts from films like "Ek ruka hua fasla" might be useful. • [Barun] Artificially scoring generic responses • [Surag] Prune generic responses from training data

  33. Extensions • [Prachi] Automatic generation of dialogue for movie given storyline and character description • [Gagan] Pre-train word embeddings on SubTle • [Arindam] RL is the best bet to avoid generic responses • [Arindam] Adversarial evaluation • [Arindam] Train additional context to add consistency?

  34. Thank You

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