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Tuesday, February 23 2016, 02:25AM  by:shuri
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source Facebook releases 1.6GB of children’s stories for training its AI
Facebook today announced that it has released the data it used to train its artificial intelligence software to understand children’s stories and predict the word that was missing from a given sentence in a story.
Thursday, January 28 2016, 02:50PM  by:shuri
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source Learning Knowledge Graphs for Question Answering through Conversational Dialog
  1. "Relation extraction systems such as NELL (Carlson et al., 2010) use ontologies to predetermine valid relation types and arguments, then scan text to fill the ontology with facts."
  2. "Open Information Extraction (Etzioni et al., 2011) avoids fixed ontologies with domain-independent linguistic features, distant supervision, and redundancy, but requires web-scale text and doesn’t improve with interacti"
  3. "Like Open IE... but first to do so from dialog"
  4. "A concept is a set of concept keywords with a common root, e.g. fmelts, melted, melting"
    "We use the Porter algorithm for stemming (Porter, 1997)."
  5. "the relation between Obama and Hawaii can be labeled with the type born-in."
  6. "the user utterance U: it’s melting because of heat relates the concepts represented by melt[ing] and heat, with the words because of appearing between the two concept keywords. We refer to because of as the relation’s intext."
  7. Sentence alignment, relations between Q-A and Supporting sentence, divide by the total number of concepts in Q-A and supporting sentence.
  8. "A userinitiative strategy always asks open-ended questions"
  9. "In contrast, a mixed-initiative strategy utilizes focused prompts (line S4 in Figure 1) to introduce potentially related concepts. KNOWBOT chooses what pair of concepts to ask about based on how discriminative they are"
  10. "The most discriminative concepts are the pair of question and support concepts that
    (1) don’t already have an edge between them, (2) satisfies the alignment constraint for the user’s answer, and (3) satisfies the alignment constraint for the fewest alternative answers."
  11. "baseline dialog strategy based on interactive query expansion (IQE). This baseline is similar to the recent knowledge acquisition dialog system of Rudnicky and Pappu (2014a; 2014b)."
    "Aasish Pappu and Alexander Rudnicky. 2014a. Knowledge acquisition strategies for goal-oriented dialog systems."
    "Aasish Pappu and Alexander Rudnicky. 2014b. Learning situated knowledge bases through dialog"
Thursday, December 17 2015, 01:11PM  by:shuri
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source Evaluating Prerequisite Qualities for Learning end-to-end Dialog Systems

"this paper proposes a collection of four tasks designed to evaluate different prerequisite qualities of end-to-end dialog systems"

"QA Dataset: Tests the ability to answer factoid questions that can be answered without relation to previous dialog. The context consists of the question only.
• Recommendation Dataset: Tests the ability to provide personalized responses to the user via recommendations (in this case, of movies) rather than universal facts as above.
• QA+Recommendation Dataset: Tests the ability of maintaining short dialogs involving both factoid and personalized content where conversational state has to be maintained.
• Reddit Dataset: Tests the ability to identify most likely replies in discussions on Reddit.
• Joint Dataset: All our tasks are dialogs. They can be combined into a single dataset, testing the ability of an end-to-end model to perform well at all skills at once."

"We employ the MemN2N architecture of Sukhbaatar et al. (2015) in our experiments, with some additional modifications to construct both long-term and short-term context memories"

"Retrieving long-termmemories For each word in the last N messages we performa hash lookup to return all long-term memories (sentences) from a database that also contain that word. Words above a certain frequency cutoff can be ignored to avoid sentences that only share syntax or unimportant words. We employ the movie knowledge base of Sec. 2.1 for our long-term memories,"

"The wholemodel is trained using stochastic gradient descent byminimizing a standard cross-entropy loss between ˆa and the true label a."


"For matching two documents supervised semantic indexing (SSI) was shown to be superior to unsupervised latent semantic indexing (LSI) (Bai et al., 2009"

"we believe this is a surprisingly strong baseline that is often neglected in evaluations"

 


"Recurrent Neural Networks (RNNs) have proven successful at several tasks involving natural language, language modeling (Mikolov et al., 2011"

"LSTMs are not known however for tasks such as QA or item recommendation, and so we expect them to find our datasets challenging."

 


"We chose the method of Bordes et al. (2014)10 as our baseline. This system learns embeddings that match questions to database entries, and then ranks the set of entries, and has been shown to achieve good performance on the WEBQUESTIONS benchmark (Berant et al., 2013)."


"Answering Factual Questions Memory Networks and the baseline QA system are the two methods that have an explicit long-term memory via access to the knowledge base (KB). On the task of answering factual questions where the answers are contained in the KB, they outperform the other methods convincingly, with LSTMS being particularly poor"

 


"Making Recommendations In this task a long-term memory does not bring any improvement, with LSTMs, Supervised Embeddings and Memory Networks all performing similarly, and all outperforming the SVD baseline."

"LSTMs performpoorly: the posts in Reddit are quite long and the memory of the LSTMis relatively short, as pointed out by Sordoni et al. (2015).

"Testing more powerful recurrent networks such as Seq2Seq or LSTMs with attention on these benchmarks remains as future wor"

Friday, June 21 2013, 05:39PM  by:shuri
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Random dialog: She was qute

him: She was qute.

me: She probably still is.

him: ...unless something horrible happened to her?!

me: are you threatening her?

him: ...should I be?