22 research outputs found
Detecting and Explaining Causes From Text For a Time Series Event
Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an explanation. To detect causal features from text, we propose a
novel method based on the Granger causality of time series between features
extracted from text such as N-grams, topics, sentiments, and their composition.
The generation of the sequence of causal entities requires a commonsense
causative knowledge base with efficient reasoning. To ensure good
interpretability and appropriate lexical usage we combine symbolic and neural
representations, using a neural reasoning algorithm trained on commonsense
causal tuples to predict the next cause step. Our quantitative and human
analysis show empirical evidence that our method successfully extracts
meaningful causality relationships between time series with textual features
and generates appropriate explanation between them.Comment: Accepted at EMNLP 201
CharManteau: Character Embedding Models For Portmanteau Creation
Portmanteaus are a word formation phenomenon where two words are combined to
form a new word. We propose character-level neural sequence-to-sequence (S2S)
methods for the task of portmanteau generation that are end-to-end-trainable,
language independent, and do not explicitly use additional phonetic
information. We propose a noisy-channel-style model, which allows for the
incorporation of unsupervised word lists, improving performance over a standard
source-to-target model. This model is made possible by an exhaustive candidate
generation strategy specifically enabled by the features of the portmanteau
task. Experiments find our approach superior to a state-of-the-art FST-based
baseline with respect to ground truth accuracy and human evaluation.Comment: Accepted for publication in EMNLP 201
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented Dialog
The task of identifying out-of-domain (OOD) input examples directly at
test-time has seen renewed interest recently due to increased real world
deployment of models. In this work, we focus on OOD detection for natural
language sentence inputs to task-based dialog systems. Our findings are
three-fold: First, we curate and release ROSTD (Real Out-of-Domain Sentences
From Task-oriented Dialog) - a dataset of 4K OOD examples for the publicly
available dataset from (Schuster et al. 2019). In contrast to existing settings
which synthesize OOD examples by holding out a subset of classes, our examples
were authored by annotators with apriori instructions to be out-of-domain with
respect to the sentences in an existing dataset. Second, we explore likelihood
ratio based approaches as an alternative to currently prevalent paradigms.
Specifically, we reformulate and apply these approaches to natural language
inputs. We find that they match or outperform the latter on all datasets, with
larger improvements on non-artificial OOD benchmarks such as our dataset. Our
ablations validate that specifically using likelihood ratios rather than plain
likelihood is necessary to discriminate well between OOD and in-domain data.
Third, we propose learning a generative classifier and computing a marginal
likelihood (ratio) for OOD detection. This allows us to use a principled
likelihood while at the same time exploiting training-time labels. We find that
this approach outperforms both simple likelihood (ratio) based and other prior
approaches. We are hitherto the first to investigate the use of generative
classifiers for OOD detection at test-time.Comment: Accepted for AAAI-2020 Main Trac
NAREOR: The Narrative Reordering Problem
Many implicit inferences exist in text depending on how it is structured that
can critically impact the text's interpretation and meaning. One such
structural aspect present in text with chronology is the order of its
presentation. For narratives or stories, this is known as the narrative order.
Reordering a narrative can impact the temporal, causal, event-based, and other
inferences readers draw from it, which in turn can have strong effects both on
its interpretation and interestingness. In this paper, we propose and
investigate the task of Narrative Reordering (NAREOR) which involves rewriting
a given story in a different narrative order while preserving its plot. We
present a dataset, NAREORC, with human rewritings of stories within ROCStories
in non-linear orders, and conduct a detailed analysis of it. Further, we
propose novel task-specific training methods with suitable evaluation metrics.
We perform experiments on NAREORC using state-of-the-art models such as BART
and T5 and conduct extensive automatic and human evaluations. We demonstrate
that although our models can perform decently, NAREOR is a challenging task
with potential for further exploration. We also investigate two applications of
NAREOR: generation of more interesting variations of stories and serving as
adversarial sets for temporal/event-related tasks, besides discussing other
prospective ones, such as for pedagogical setups related to language skills
like essay writing and applications to medicine involving clinical narratives.Comment: Accepted to AAAI 202