17,746 research outputs found
Improved Neural Relation Detection for Knowledge Base Question Answering
Relation detection is a core component for many NLP applications including
Knowledge Base Question Answering (KBQA). In this paper, we propose a
hierarchical recurrent neural network enhanced by residual learning that
detects KB relations given an input question. Our method uses deep residual
bidirectional LSTMs to compare questions and relation names via different
hierarchies of abstraction. Additionally, we propose a simple KBQA system that
integrates entity linking and our proposed relation detector to enable one
enhance another. Experimental results evidence that our approach achieves not
only outstanding relation detection performance, but more importantly, it helps
our KBQA system to achieve state-of-the-art accuracy for both single-relation
(SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.Comment: Accepted by ACL 2017 (updated for camera-ready
Unsupervised Domain Adaptation on Reading Comprehension
Reading comprehension (RC) has been studied in a variety of datasets with the
boosted performance brought by deep neural networks. However, the
generalization capability of these models across different domains remains
unclear. To alleviate this issue, we are going to investigate unsupervised
domain adaptation on RC, wherein a model is trained on labeled source domain
and to be applied to the target domain with only unlabeled samples. We first
show that even with the powerful BERT contextual representation, the
performance is still unsatisfactory when the model trained on one dataset is
directly applied to another target dataset. To solve this, we provide a novel
conditional adversarial self-training method (CASe). Specifically, our approach
leverages a BERT model fine-tuned on the source dataset along with the
confidence filtering to generate reliable pseudo-labeled samples in the target
domain for self-training. On the other hand, it further reduces domain
distribution discrepancy through conditional adversarial learning across
domains. Extensive experiments show our approach achieves comparable accuracy
to supervised models on multiple large-scale benchmark datasets.Comment: 8 pages, 6 figures, 5 tables, Accepted by AAAI 202
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
QuAC : Question Answering in Context
We present QuAC, a dataset for Question Answering in Context that contains
14K information-seeking QA dialogs (100K questions in total). The dialogs
involve two crowd workers: (1) a student who poses a sequence of freeform
questions to learn as much as possible about a hidden Wikipedia text, and (2) a
teacher who answers the questions by providing short excerpts from the text.
QuAC introduces challenges not found in existing machine comprehension
datasets: its questions are often more open-ended, unanswerable, or only
meaningful within the dialog context, as we show in a detailed qualitative
evaluation. We also report results for a number of reference models, including
a recently state-of-the-art reading comprehension architecture extended to
model dialog context. Our best model underperforms humans by 20 F1, suggesting
that there is significant room for future work on this data. Dataset, baseline,
and leaderboard available at http://quac.ai.Comment: EMNLP Camera Read
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