423 research outputs found
Graph-Query Suggestions for Knowledge Graph Exploration
We consider the task of exploratory search through graph queries on knowledge graphs. We propose to assist the user by expanding the query with intuitive suggestions to provide a more informative (full) query that can retrieve more detailed and relevant answers. To achieve this result, we propose a model that can bridge graph search paradigms with well-established techniques for information-retrieval. Our approach does not require any additional knowledge from the user and builds on principled language modelling approaches. We empirically show the effectiveness and efficiency of our approach on a large knowledge graph and how our suggestions are able to help build more complete and informative queries
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
The problem of answering questions using knowledge from pre-trained language
models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA
context (question and answer choice), methods need to (i) identify relevant
knowledge from large KGs, and (ii) perform joint reasoning over the QA context
and KG. In this work, we propose a new model, QA-GNN, which addresses the above
challenges through two key innovations: (i) relevance scoring, where we use LMs
to estimate the importance of KG nodes relative to the given QA context, and
(ii) joint reasoning, where we connect the QA context and KG to form a joint
graph, and mutually update their representations through graph neural networks.
We evaluate our model on QA benchmarks in the commonsense (CommonsenseQA,
OpenBookQA) and biomedical (MedQA-USMLE) domains. QA-GNN outperforms existing
LM and LM+KG models, and exhibits capabilities to perform interpretable and
structured reasoning, e.g., correctly handling negation in questions.Comment: NAACL 2021. Code & data available at
https://github.com/michiyasunaga/qagn
Query Resolution for Conversational Search with Limited Supervision
In this work we focus on multi-turn passage retrieval as a crucial component
of conversational search. One of the key challenges in multi-turn passage
retrieval comes from the fact that the current turn query is often
underspecified due to zero anaphora, topic change, or topic return. Context
from the conversational history can be used to arrive at a better expression of
the current turn query, defined as the task of query resolution. In this paper,
we model the query resolution task as a binary term classification problem: for
each term appearing in the previous turns of the conversation decide whether to
add it to the current turn query or not. We propose QuReTeC (Query Resolution
by Term Classification), a neural query resolution model based on bidirectional
transformers. We propose a distant supervision method to automatically generate
training data by using query-passage relevance labels. Such labels are often
readily available in a collection either as human annotations or inferred from
user interactions. We show that QuReTeC outperforms state-of-the-art models,
and furthermore, that our distant supervision method can be used to
substantially reduce the amount of human-curated data required to train
QuReTeC. We incorporate QuReTeC in a multi-turn, multi-stage passage retrieval
architecture and demonstrate its effectiveness on the TREC CAsT dataset.Comment: SIGIR 2020 full conference pape
Towards Ontological Support for Journalistic Angles
Journalism relies more and more on information and communication technology (ICT). New journalistic ICT platforms continuously harvest potentially news-related information from the internet and try to make it useful for journalists. Because the information sources and formats vary widely, knowledge graphs are emerging as a preferred technology for integrating, enriching, and preparing journalistic information. The paper explores how journalistic knowledge graphs can be augmented with support for news angles, in order to help journalists detect news-worthy events and present them in ways that will interest the intended audience. We argue that finding newsworthy angles on news-related information is important as an example of a more general problem in information science: that of finding the most interesting events and situations in big data sets and presenting those events and situations in the most interesting ways.acceptedVersio
Generalized Weak Supervision for Neural Information Retrieval
Neural ranking models (NRMs) have demonstrated effective performance in
several information retrieval (IR) tasks. However, training NRMs often requires
large-scale training data, which is difficult and expensive to obtain. To
address this issue, one can train NRMs via weak supervision, where a large
dataset is automatically generated using an existing ranking model (called the
weak labeler) for training NRMs. Weakly supervised NRMs can generalize from the
observed data and significantly outperform the weak labeler. This paper
generalizes this idea through an iterative re-labeling process, demonstrating
that weakly supervised models can iteratively play the role of weak labeler and
significantly improve ranking performance without using manually labeled data.
The proposed Generalized Weak Supervision (GWS) solution is generic and
orthogonal to the ranking model architecture. This paper offers four
implementations of GWS: self-labeling, cross-labeling, joint cross- and
self-labeling, and greedy multi-labeling. GWS also benefits from a query
importance weighting mechanism based on query performance prediction methods to
reduce noise in the generated training data. We further draw a theoretical
connection between self-labeling and Expectation-Maximization. Our experiments
on two passage retrieval benchmarks suggest that all implementations of GWS
lead to substantial improvements compared to weak supervision in all cases
Text summarization towards scientific information extraction
Despite the exponential growth in scientific textual content, research publications are still the primary means for disseminating vital discoveries to experts within their respective fields. These texts are predominantly written for human consumption resulting in two primary challenges; experts cannot efficiently remain well-informed to leverage the latest discoveries, and applications that rely on valuable insights buried in these texts cannot effectively build upon published results. As a result, scientific progress stalls. Automatic Text Summarization (ATS) and Information Extraction (IE) are two essential fields that address this problem. While the two research topics are often studied independently, this work proposes to look at ATS in the context of IE, specifically in relation to Scientific IE. However, Scientific IE faces several challenges, chiefly, the scarcity of relevant entities and insufficient training data. In this paper, we focus on extractive ATS, which identifies the most valuable sentences from textual content for the purpose of ultimately extracting scientific relations. We account for the associated challenges by means of an ensemble method through the integration of three weakly supervised learning models, one for each entity of the target relation. It is important to note that while the relation is well defined, we do not require previously annotated data for the entities composing the relation. Our central objective is to generate balanced training data, which many advanced natural language processing models require. We apply our idea in the domain of materials science, extracting the polymer-glass transition temperature relation and achieve 94.7% recall (i.e., sentences that contain relations annotated by humans), while reducing the text by 99.3% of the original document
Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
Multimodal machine learning is a vibrant multi-disciplinary research field
that aims to design computer agents with intelligent capabilities such as
understanding, reasoning, and learning through integrating multiple
communicative modalities, including linguistic, acoustic, visual, tactile, and
physiological messages. With the recent interest in video understanding,
embodied autonomous agents, text-to-image generation, and multisensor fusion in
application domains such as healthcare and robotics, multimodal machine
learning has brought unique computational and theoretical challenges to the
machine learning community given the heterogeneity of data sources and the
interconnections often found between modalities. However, the breadth of
progress in multimodal research has made it difficult to identify the common
themes and open questions in the field. By synthesizing a broad range of
application domains and theoretical frameworks from both historical and recent
perspectives, this paper is designed to provide an overview of the
computational and theoretical foundations of multimodal machine learning. We
start by defining two key principles of modality heterogeneity and
interconnections that have driven subsequent innovations, and propose a
taxonomy of 6 core technical challenges: representation, alignment, reasoning,
generation, transference, and quantification covering historical and recent
trends. Recent technical achievements will be presented through the lens of
this taxonomy, allowing researchers to understand the similarities and
differences across new approaches. We end by motivating several open problems
for future research as identified by our taxonomy
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