Exploring Topic Continuation Follow-up Questions using Machine Learning
نویسندگان
چکیده
Some of the Follow-Up Questions (FU Q) that an Interactive Question Answering (IQA) system receives are not topic shifts, but rather continuations of the previous topic. In this paper, we propose an empirical framework to explore such questions, with two related goals in mind: (1) modeling the different relations that hold between the FU Q’s answer and either the FU Q or the preceding dialogue, and (2) showing how this model can be used to identify the correct answer among several answer candidates. For both cases, we use Logistic Regression Models that we learn from real IQA data collected through a live system. We show that by adding dialogue context features and features based on sequences of domain-specific actions that represent the questions and answers, we obtain important additional predictors for the model, and improve the accuracy with which our system finds correct answers.
منابع مشابه
Towards an Empirically Motivated Typology of Follow-Up Questions: The Role of Dialogue Context
A central problem in Interactive Question Answering (IQA) is how to answer Follow-Up Questions (FU Qs), possibly by taking advantage of information from the dialogue context. We assume that FU Qs can be classified into specific types which determine if and how the correct answer relates to the preceding dialogue. The main goal of this paper is to propose an empirically motivated typology of FU ...
متن کاملUsing Machine Learning ARIMA to Predict the Price of Cryptocurrencies
The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model ...
متن کاملClassifying Legal Questions into Topic Areas Using Machine Learning
In this paper we describe the steps taken to build a machine learning classifier that successfully classifies legal questions into the most relevant practice area. We have created 16 different general categories that legal questions fall into. Categorizing legal questions into the correct practice area has many useful applications such as facilitating improved realtime feedback, information ret...
متن کامل∗NQSotA Continuation Curriculum Learning with Question Answering on the SQuAD Dataset
We implement a slightly simplified Bi-Directional Attention Flow Model[4] and a slightly modified Multi-Perspective Context Matching[6] model for Question Answering on the SQuAD dataset. In the Multi-Perspective model, we add perspective matching between forward and backward contexts. We omit the character-level embeddings of both models, and make a few other small simplifications. We briefly l...
متن کاملRefinery: An Open Source Topic Modeling Web Platform
We introduce Refinery, an open source platform for exploring large text document collections with topic models. Refinery is a standalone web application driven by a graphical interface, so it is usable by those without machine learning or programming expertise. Users can interactively organize articles by topic and also refine this organization with phrase-level analysis. Under the hood, we tra...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009