401 research outputs found
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks
For a company looking to provide delightful user experiences, it is of
paramount importance to take care of any customer issues. This paper proposes
COTA, a system to improve speed and reliability of customer support for end
users through automated ticket classification and answers selection for support
representatives. Two machine learning and natural language processing
techniques are demonstrated: one relying on feature engineering (COTA v1) and
the other exploiting raw signals through deep learning architectures (COTA v2).
COTA v1 employs a new approach that converts the multi-classification task into
a ranking problem, demonstrating significantly better performance in the case
of thousands of classes. For COTA v2, we propose an Encoder-Combiner-Decoder, a
novel deep learning architecture that allows for heterogeneous input and output
feature types and injection of prior knowledge through network architecture
choices. This paper compares these models and their variants on the task of
ticket classification and answer selection, showing model COTA v2 outperforms
COTA v1, and analyzes their inner workings and shortcomings. Finally, an A/B
test is conducted in a production setting validating the real-world impact of
COTA in reducing issue resolution time by 10 percent without reducing customer
satisfaction
Filling Conversation Ellipsis for Better Social Dialog Understanding
The phenomenon of ellipsis is prevalent in social conversations. Ellipsis
increases the difficulty of a series of downstream language understanding
tasks, such as dialog act prediction and semantic role labeling. We propose to
resolve ellipsis through automatic sentence completion to improve language
understanding. However, automatic ellipsis completion can result in output
which does not accurately reflect user intent. To address this issue, we
propose a method which considers both the original utterance that has ellipsis
and the automatically completed utterance in dialog act and semantic role
labeling tasks. Specifically, we first complete user utterances to resolve
ellipsis using an end-to-end pointer network model. We then train a prediction
model using both utterances containing ellipsis and our automatically completed
utterances. Finally, we combine the prediction results from these two
utterances using a selection model that is guided by expert knowledge. Our
approach improves dialog act prediction and semantic role labeling by 1.3% and
2.5% in F1 score respectively in social conversations. We also present an
open-domain human-machine conversation dataset with manually completed user
utterances and annotated semantic role labeling after manual completion.Comment: Accepted to AAAI 202
Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey
The emergence of natural language processing has revolutionized the way users
interact with tabular data, enabling a shift from traditional query languages
and manual plotting to more intuitive, language-based interfaces. The rise of
large language models (LLMs) such as ChatGPT and its successors has further
advanced this field, opening new avenues for natural language processing
techniques. This survey presents a comprehensive overview of natural language
interfaces for tabular data querying and visualization, which allow users to
interact with data using natural language queries. We introduce the fundamental
concepts and techniques underlying these interfaces with a particular emphasis
on semantic parsing, the key technology facilitating the translation from
natural language to SQL queries or data visualization commands. We then delve
into the recent advancements in Text-to-SQL and Text-to-Vis problems from the
perspectives of datasets, methodologies, metrics, and system designs. This
includes a deep dive into the influence of LLMs, highlighting their strengths,
limitations, and potential for future improvements. Through this survey, we aim
to provide a roadmap for researchers and practitioners interested in developing
and applying natural language interfaces for data interaction in the era of
large language models.Comment: 20 pages, 4 figures, 5 tables. Submitted to IEEE TKD
Natural Language Interfaces to Data
Recent advances in NLU and NLP have resulted in renewed interest in natural
language interfaces to data, which provide an easy mechanism for non-technical
users to access and query the data. While early systems evolved from keyword
search and focused on simple factual queries, the complexity of both the input
sentences as well as the generated SQL queries has evolved over time. More
recently, there has also been a lot of focus on using conversational interfaces
for data analytics, empowering a line of non-technical users with quick
insights into the data. There are three main challenges in natural language
querying (NLQ): (1) identifying the entities involved in the user utterance,
(2) connecting the different entities in a meaningful way over the underlying
data source to interpret user intents, and (3) generating a structured query in
the form of SQL or SPARQL.
There are two main approaches for interpreting a user's NLQ. Rule-based
systems make use of semantic indices, ontologies, and KGs to identify the
entities in the query, understand the intended relationships between those
entities, and utilize grammars to generate the target queries. With the
advances in deep learning (DL)-based language models, there have been many
text-to-SQL approaches that try to interpret the query holistically using DL
models. Hybrid approaches that utilize both rule-based techniques as well as DL
models are also emerging by combining the strengths of both approaches.
Conversational interfaces are the next natural step to one-shot NLQ by
exploiting query context between multiple turns of conversation for
disambiguation. In this article, we review the background technologies that are
used in natural language interfaces, and survey the different approaches to
NLQ. We also describe conversational interfaces for data analytics and discuss
several benchmarks used for NLQ research and evaluation.Comment: The full version of this manuscript, as published by Foundations and
Trends in Databases, is available at http://dx.doi.org/10.1561/190000007
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