8,295 research outputs found
Aspect-Controlled Neural Argument Generation
We rely on arguments in our daily lives to deliver our opinions and base them
on evidence, making them more convincing in turn. However, finding and
formulating arguments can be challenging. In this work, we train a language
model for argument generation that can be controlled on a fine-grained level to
generate sentence-level arguments for a given topic, stance, and aspect. We
define argument aspect detection as a necessary method to allow this
fine-granular control and crowdsource a dataset with 5,032 arguments annotated
with aspects. Our evaluation shows that our generation model is able to
generate high-quality, aspect-specific arguments. Moreover, these arguments can
be used to improve the performance of stance detection models via data
augmentation and to generate counter-arguments. We publish all datasets and
code to fine-tune the language model
How did the discussion go: Discourse act classification in social media conversations
We propose a novel attention based hierarchical LSTM model to classify
discourse act sequences in social media conversations, aimed at mining data
from online discussion using textual meanings beyond sentence level. The very
uniqueness of the task is the complete categorization of possible pragmatic
roles in informal textual discussions, contrary to extraction of
question-answers, stance detection or sarcasm identification which are very
much role specific tasks. Early attempt was made on a Reddit discussion
dataset. We train our model on the same data, and present test results on two
different datasets, one from Reddit and one from Facebook. Our proposed model
outperformed the previous one in terms of domain independence; without using
platform-dependent structural features, our hierarchical LSTM with word
relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively
to predict discourse roles of comments in Reddit and Facebook discussions.
Efficiency of recurrent and convolutional architectures in order to learn
discursive representation on the same task has been presented and analyzed,
with different word and comment embedding schemes. Our attention mechanism
enables us to inquire into relevance ordering of text segments according to
their roles in discourse. We present a human annotator experiment to unveil
important observations about modeling and data annotation. Equipped with our
text-based discourse identification model, we inquire into how heterogeneous
non-textual features like location, time, leaning of information etc. play
their roles in charaterizing online discussions on Facebook
Corpus Wide Argument Mining -- a Working Solution
One of the main tasks in argument mining is the retrieval of argumentative
content pertaining to a given topic. Most previous work addressed this task by
retrieving a relatively small number of relevant documents as the initial
source for such content. This line of research yielded moderate success, which
is of limited use in a real-world system. Furthermore, for such a system to
yield a comprehensive set of relevant arguments, over a wide range of topics,
it requires leveraging a large and diverse corpus in an appropriate manner.
Here we present a first end-to-end high-precision, corpus-wide argument mining
system. This is made possible by combining sentence-level queries over an
appropriate indexing of a very large corpus of newspaper articles, with an
iterative annotation scheme. This scheme addresses the inherent label bias in
the data and pinpoints the regions of the sample space whose manual labeling is
required to obtain high-precision among top-ranked candidates
Representation learning on relational data
Humans utilize information about relationships or interactions between objects for orientation in various situations. For example, we trust our friend circle recommendations, become friends with the people we already have shared friends with, or adapt opinions as a result of interactions with other people.
In many Machine Learning applications, we also know about relationships, which bear essential information for the use-case.
Recommendations in social media, scene understanding in computer vision, or traffic prediction are few examples where relationships play a crucial role in the application.
In this thesis, we introduce methods taking relationships into account and demonstrate their benefits for various problems.
A large number of problems, where relationship information plays a central role, can be approached by modeling data by a graph structure and by task formulation as a prediction problem on the graph.
In the first part of the thesis, we tackle the problem of node classification from various directions. We start with unsupervised learning approaches, which differ by assumptions they make about the relationship's meaning in the graph.
For some applications such as social networks, it is a feasible assumption that densely connected nodes are similar. On the other hand, if we want to predict passenger traffic for the airport based on its flight connections, similar nodes are not necessarily positioned close to each other in the graph and more likely have comparable neighborhood patterns.
Furthermore, we introduce novel methods for classification and regression in a semi-supervised setting, where labels of interest are known for a fraction of nodes. We use the known prediction targets and information about how nodes connect to learn the relationships' meaning and their effect on the final prediction.
In the second part of the thesis, we deal with the problem of graph matching. Our first use-case is the alignment of different geographical maps, where the focus lies on the real-life setting. We introduce a robust method that can learn to ignore the noise in the data.
Next, our focus moves to the field of Entity Alignment in different Knowledge Graphs.
We analyze the process of manual data annotation and propose a setting and algorithms to accelerate this labor-intensive process.
Furthermore, we point to the several shortcomings in the empirical evaluations, make several suggestions on how to improve it, and extensively analyze existing approaches for the task.
The next part of the thesis is dedicated to the research direction dealing with automatic extraction and search of arguments, known as Argument Mining. We propose a novel approach for identifying arguments and demonstrate how it can make use of relational information. We apply our method to identify arguments in peer-reviews for scientific publications and show that arguments are essential for the decision process. Furthermore, we address the problem of argument search and introduce a novel approach that retrieves relevant and original arguments for the user's queries.
Finally, we propose an approach for subspace clustering, which can deal with large datasets and assign new objects to the found clusters. Our method learns the relationships between objects and performs the clustering on the resulting graph
TACAM: Topic And Context Aware Argument Mining
In this work we address the problem of argument search. The purpose of
argument search is the distillation of pro and contra arguments for requested
topics from large text corpora. In previous works, the usual approach is to use
a standard search engine to extract text parts which are relevant to the given
topic and subsequently use an argument recognition algorithm to select
arguments from them. The main challenge in the argument recognition task, which
is also known as argument mining, is that often sentences containing arguments
are structurally similar to purely informative sentences without any stance
about the topic. In fact, they only differ semantically. Most approaches use
topic or search term information only for the first search step and therefore
assume that arguments can be classified independently of a topic. We argue that
topic information is crucial for argument mining, since the topic defines the
semantic context of an argument. Precisely, we propose different models for the
classification of arguments, which take information about a topic of an
argument into account. Moreover, to enrich the context of a topic and to let
models understand the context of the potential argument better, we integrate
information from different external sources such as Knowledge Graphs or
pre-trained NLP models. Our evaluation shows that considering topic
information, especially in connection with external information, provides a
significant performance boost for the argument mining task
Multi-Task Attentive Residual Networks for Argument Mining
We explore the use of residual networks and neural attention for argument
mining and in particular link prediction. The method we propose makes no
assumptions on document or argument structure. We propose a residual
architecture that exploits attention, multi-task learning, and makes use of
ensemble. We evaluate it on a challenging data set consisting of user-generated
comments, as well as on two other datasets consisting of scientific
publications. On the user-generated content dataset, our model outperforms
state-of-the-art methods that rely on domain knowledge. On the scientific
literature datasets it achieves results comparable to those yielded by
BERT-based approaches but with a much smaller model size.Comment: 12 pages, 2 figures, submitted to IEEE Transactions on Neural
Networks and Learning System
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Different Flavors of Attention Networks for Argument Mining
International audienceArgument mining is a rising area of Natural Language Processing (NLP) concerned with the automatic recognition and interpretation of argument components and their relations. Neural models are by now mature technologies to be exploited for automating the argument mining tasks, despite the issue of data sparseness. This could ease much of the manual effort involved in these tasks, taking into account heterogeneous types of texts and topics. In this work, we evaluate different attention mechanisms applied over a state-of-the-art architecture for sequence labeling. We assess the impact of different flavors of attention in the task of argument component detection over two datasets: essays and legal domain. We show that attention not only models the problem better but also supports interpretability
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