Question Answering with Subgraph Embeddings Question Answering with Subgraph Embeddings
Paper summary #### Introduction * Open-domain Question Answering (Open QA) - efficiently querying large-scale knowledge base(KB) using natural language. * Two main approaches: * Information Retrieval * Transform question (in natural language) into a valid query(in terms of KB) to get a broad set of candidate answers. * Perform fine-grained detection on candidate answers. * Semantic Parsing * Interpret the correct meaning of the question and convert it into an exact query. * Limitations: * Human intervention to create lexicon, grammar, and schema. * This work builds upon the previous work where an embedding model learns low dimensional vector representation of words and symbols. * [Link]( to the paper. #### Task Definition * Input - Training set of questions (paired with answers). * KB providing a structure among the answers. * Answers are entities in KB and questions are strings with one identified KB entity. * The paper has used FREEBASE as the KB. * Datasets * WebQuestions - Built using FREEBASE, Google Suggest API, and Mechanical Turk. * FREEBASE triplets transformed into questions. * Clue Web Extractions dataset with entities linked with FREEBASE triplets. * Dataset of paraphrased questions using WIKIANSWERS. #### Embedding Questions and Answers * Model learns low-dimensional vector embeddings of words in question entities and relation types of FREEBASE such that questions and their answers are represented close to each other in the joint embedding space. * Scoring function $S(q, a)$, where $q$ is a question and $a$ is an answer, generates high score if $a$ answers $q$. * $S(q, a) = f(q)^{T} . g(a)$ * $f(q)$ maps question to embedding space. * $f(q) = W \phi (q)$ * $W$ is a matrix of dimension $K * N$ * $K$ - dimension of embedding space (hyper parameter). * $N$ - total number of words/entities/relation types. * $\psi(q)$ - Sparse Vector encoding the number of times a word appears in $q$. * Similarly, $g(a) = W \psi (a)$ maps answer to embedding space. * $\psi(a)$ gives answer representation, as discussed below. #### Possible Representations of Candidate Answers * Answer represented as a **single entity** from FREEBASE and TBD is a one-of-N encoded vector. * Answer represented as a **path** from question to answer. The paper considers only one or two hop paths resulting in 3-of-N or 4-of-N encoded vectors(middle entities are not recorded). * Encode the above two representations using **subgraph representation** which represents both the path and the entire subgraph of entities connected to answer entity as a subgraph. Two embedding representations are used to differentiate between entities in path and entities in the subgraph. * SubGraph approach is based on the hypothesis that including more information about the answers would improve results. #### Training and Loss Function * Minimize margin based ranking loss to learn matrix $W$. * Stochastic Gradient Descent, multi-threaded with Hogwild. #### Multitask Training of Embeddings * To account for a large number of synthetically generated questions, the paper also multi-tasks the training of model with paraphrased prediction. * Scoring function $S_{prp} (q1, q2) = f(q1)^{T} f(q2)$, where $f$ uses the same weight matrix $W$ as before. * High score is assigned if $q1$ and $q2$ belong to same paraphrase cluster. * Additionally, the model multitasks the task of mapping embeddings of FREEBASE entities (mids) to actual words. #### Inference * For each question, a candidate set is generated. * The answer (from candidate set) with the highest set is reported as the correct answer. * Candidate set generation strategy * $C_1$ - All KB triplets containing the KB entity from the question forms a candidate set. Answers would be limited to 1-hop paths. * $C_2$ - Rank all relation types and keep top 10 types and add only those 2-hop candidates where the selected relations appear in the path. #### Results * $C_2$ strategy outperforms $C_1$ approach supporting the hypothesis that a richer representation for answers can store more information. * Proposed approach outperforms the baseline methods but is outperformed by an ensemble of proposed approach with semantic parsing via paraphrasing model.
Question Answering with Subgraph Embeddings
Antoine Bordes and Sumit Chopra and Jason Weston
arXiv e-Print archive - 2014 via Local arXiv
Keywords: cs.CL


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