- The paper presents a suite of benchmark tasks to evaluate end-to-end dialogue systems such that performing well on the tasks is a necessary (but not sufficient) condition for a fully functional dialogue agent.
- Link to the paper
- Created using large-scale real-world sources - OMDB (Open Movie Database), MovieLens and Reddit.
- Consists of ~75K movie entities and ~3.5M training examples.
- Answering Factoid Questions without relation to the previous dialogue.
- KB(Knowledge Base) created using OMDB and stored as triplets of the form (Entity, Relation, Entity).
- Question (in Natural Language Form) generated by creating templates using SimpleQuestions
- Instead of giving out just 1 response, the system ranks all the answers in order of their relevance.
- Providing personalised responses to the user via recommendation instead of providing universal facts as in case 1.
- MovieLens dataset with a user x item matrix of ratings.
- Statements (for any user) are generated by sampling highly ranked movies by the user and forming a statement about these movies using natural language templates.
- Like the previous case, a list of ranked responses is generated.
QA + Recommendation Task
- Maintaining short dialogues involving both factoid and personalised content.
- Dataset consists of short conversations of 3 exchanges (3 from each participant).
Reddit Discussion Task
- Identify most likely response is discussions on Reddit.
- Data processed to flatten the potential conversation so that it appears to be a two participant conversation.
- Combines all the previous tasks into one single task to test all the skills at once.
- Memory Networks - Comprises of a memory component that includes both long term memory and short term context.
- Supervised Embedding Models - Sum the word embeddings of the input and the target independently and compare them with a similarity metric.
- Recurrent Language Models - RNN, LSTM, SeqToSeq
- Question Answering Systems - Systems that answer natural language questions by converting them into search queries over a KB.
- SVD(Singular Value Decomposition) - Standard benchmark for recommendation.
- Information Retrieval Models - Given a message, find the most similar message in the training dataset and report its output or find a most similar response to input directly.
- QA System > Memory Networks > Supervised Embeddings > LSTM
- Supervised Embeddings > Memory Networks > LSTM > SVD
Task Involving Dialog History
- QA + Recommendation Task and Reddit Discussion Task
- Memory Networks > Supervised Embeddings > LSTM
- Supervised word embeddings perform very poorly even when using a large number of dimensions (2000 dimensions).
- Memory Networks perform better than embedding models as they can utilise the local context and the long-term memory. But they do not perform as well on standalone QA tasks.