公开(公告)号：WO2017168252A1

公开(公告)日：2017-10-05

申请号：PCT/IB2017/000457

申请日：2017-03-31

Applicant: MALUUBA INC.

Inventor： HE, Jing ,  MERHEB-HARB, Jean ,  YE, Zheng ,  SULEMAN, Kaheer

IPC: G06F17/30 ,  G06F17/27 ,  G06N3/02

Abstract: Disclosed embodiments include systems and methods relevant to improvements to natural language processing used to determine an intent and one or more associated parameters from a given input string. In an example, an input string is received and first and second different n-grams are applied to the input string. Recurrent neural network models are then used to generate output data based in part on the first and second different n-grams. Intent detection and semantic labeling are applied to the output of the recurrent neural network models.

Abstract translation: 所公开的实施例包括与用于确定来自给定输入串的意图和一个或多个关联参数的自然语言处理的改进相关的系统和方法。 在一个例子中，接收输入字符串并将第一和第二不同n元素应用于输入字符串。 递归神经网络模型然后用于部分地基于第一和第二不同n元生成输出数据。 意向检测和语义标注应用于递归神经网络模型的输出。

公开(公告)号：WO2017201195A9

公开(公告)日：2017-11-23

申请号：PCT/US2017/033159

申请日：2017-05-17

Applicant: MALUUBA INC.

Inventor： TRISCHLER, Adam ,  BACHMAN, Philip ,  YUAN, Xingdi ,  YE, Zheng ,  SORDONI, Alessandro

IPC: G06F17/27

Abstract: Described herein are systems and methods for providing a natural language comprehension system that employs a two-stage process for machine comprehension of text. The first stage indicates words in one or more text passages that potentially answer a question. The first stage outputs a set of candidate answers for the question, along with a first probability of correctness for each candidate answer. The second stage forms one or more hypotheses by inserting each candidate answer into the question and determines whether a sematic relationship exists between each hypothesis and each sentence in the text. The second processing circuitry generates a second probability of correctness for each candidate answer and combines the first probability with the second probability to produce a score that is used to rank the candidate answers. The candidate answer with the highest score is selected as a predicted answer.

公开(公告)号：WO2017161189A1

公开(公告)日：2017-09-21

申请号：PCT/US2017/022812

申请日：2017-03-16

Applicant: MALUUBA INC. ,  BACHMAN, Philip

Inventor： BACHMAN, Philip ,  TRISCHLER, Adam ,  YE, Zheng ,  YUAN, Xingdi

IPC: G06F17/28 ,  G06N3/02 ,  G06N99/00

CPC classification number: G06N3/08 ,  G06F17/277 ,  G06F17/2785 ,  G06N3/0454 ,  G06N5/022 ,  G06N5/04 ,  G06N20/10

Abstract: Examples of the present disclosure provide systems and methods relating to a machine comprehension test with a learning-based approach, harnessing neural networks arranged in a parallel hierarchy. This parallel hierarchy enables the model to compare the passage, question, and answer from a variety of perspectives, as opposed to using a manually designed set of features. Perspectives may range from the word level to sentence fragments to sequences of sentences, and networks operate on word-embedding representations of text. A training methodology for small data is also provided.

Abstract translation: 本公开的示例提供了与利用基于学习的方法的机器理解测试相关的系统和方法，利用了排列在并行分层结构中的神经网络。 这种平行的层次结构使模型能够从各种角度比较段落，问题和答案，而不是使用手动设计的一组功能。 透视可以从单词级别到句子片段到句子序列，并且网络对文本的词语嵌入表示进行操作。 还提供了小数据的培训方法。

公开(公告)号：WO2017201195A1

公开(公告)日：2017-11-23

申请号：PCT/US2017/033159

申请日：2017-05-17

Applicant: MALUUBA INC.

Inventor： TRISCHLER, Adam ,  BACHMAN, Philip ,  YUAN, Xingdi ,  YE, Zheng ,  SORDONI, Alessandro

IPC: G06F17/27

Abstract: Described herein are systems and methods for providing a natural language comprehension system that employs a two-stage process for machine comprehension of text. The first stage indicates words in one or more text passages that potentially answer a question. The first stage outputs a set of candidate answers for the question, along with a first probability of correctness for each candidate answer. The second stage forms one or more hypotheses by inserting each candidate answer into the question and determines whether a sematic relationship exists between each hypothesis and each sentence in the text. The second processing circuitry generates a second probability of correctness for each candidate answer and combines the first probability with the second probability to produce a score that is used to rank the candidate answers. The candidate answer with the highest score is selected as a predicted answer.

Abstract translation: 这里描述的是用于提供自然语言理解系统的系统和方法，该自然语言理解系统采用用于机器理解文本的两阶段过程。 第一阶段指出可能回答问题的一个或多个文本段落中的单词。 第一阶段输出一组针对该问题的候选答案，以及每个候选答案的第一个正确概率。 第二阶段通过将每个候选答案插入问题形成一个或多个假设，并确定每个假设与文本中每个句子之间是否存在语义关系。 第二处理电路针对每个候选答案生成第二正确率概率，并将第一概率与第二概率组合以产生用于对候选答案进行排序的分数。 具有最高分数的候选答案被选为预测答案。