311 research outputs found
Verb Physics: Relative Physical Knowledge of Actions and Objects
Learning commonsense knowledge from natural language text is nontrivial due
to reporting bias: people rarely state the obvious, e.g., "My house is bigger
than me." However, while rarely stated explicitly, this trivial everyday
knowledge does influence the way people talk about the world, which provides
indirect clues to reason about the world. For example, a statement like, "Tyler
entered his house" implies that his house is bigger than Tyler.
In this paper, we present an approach to infer relative physical knowledge of
actions and objects along five dimensions (e.g., size, weight, and strength)
from unstructured natural language text. We frame knowledge acquisition as
joint inference over two closely related problems: learning (1) relative
physical knowledge of object pairs and (2) physical implications of actions
when applied to those object pairs. Empirical results demonstrate that it is
possible to extract knowledge of actions and objects from language and that
joint inference over different types of knowledge improves performance.Comment: 11 pages, published in Proceedings of ACL 201
MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations
We study conversational dialog in which there are many possible responses to
a given history. We present the MultiTalk Dataset, a corpus of over 320,000
sentences of written conversational dialog that balances a high branching
factor (10) with several conversation turns (6) through selective branch
continuation. We make multiple contributions to study dialog generation in the
highly branching setting. In order to evaluate a diverse set of generations, we
propose a simple scoring algorithm, based on bipartite graph matching, to
optimally incorporate a set of diverse references. We study multiple language
generation tasks at different levels of predictive conversation depth, using
textual attributes induced automatically from pretrained classifiers. Our
culminating task is a challenging theory of mind problem, a controllable
generation task which requires reasoning about the expected reaction of the
listener.Comment: 7 pages, AAAI-2
Probing two-path electron quantum interference in strong-field ionization with time-correlation filtering
Attosecond dynamics in strong-field tunnel ionization are encoded in intricate holographic patterns in the photoelectron momentum distributions. These patterns show the interference between two or more superposed quantum electron trajectories, which are defined by their ionization times and subsequent evolution in the laser field. We determine the ionization time separation between interfering pairs of electron orbits by performing a differential Fourier analysis on the measured momentum spectrum. We identify electron holograms formed by trajectory pairs whose ionization times are separated by less than a single quarter cycle, between a quarter cycle and half cycle, between a half cycle and three fourths of a cycle, and a full cycle apart. We compare our experimental results to the predictions of the Coulomb quantum orbit strong-field approximation (CQSFA) with significant success. We also time-filter the CQSFA trajectory calculations to demonstrate the validity of the technique on spectra with known time correlations. As a general analysis technique, the filter can be applied to all energy- and angularly resolved data sets to recover time correlations between interfering electron pathways, providing an important tool to analyze any strong-field ionization spectra. Moreover, it is independent of theory and can be applied directly to experiments, without the need of a direct comparison with orbit-based theoretical methods
Paragraph-level Commonsense Transformers with Recurrent Memory
Human understanding of narrative texts requires making commonsense inferences
beyond what is stated explicitly in the text. A recent model, COMET, can
generate such implicit commonsense inferences along several dimensions such as
pre- and post-conditions, motivations, and mental states of the participants.
However, COMET was trained on commonsense inferences of short phrases, and is
therefore discourse-agnostic. When presented with each sentence of a
multi-sentence narrative, it might generate inferences that are inconsistent
with the rest of the narrative.
We present the task of discourse-aware commonsense inference. Given a
sentence within a narrative, the goal is to generate commonsense inferences
along predefined dimensions, while maintaining coherence with the rest of the
narrative. Such large-scale paragraph-level annotation is hard to get and
costly, so we use available sentence-level annotations to efficiently and
automatically construct a distantly supervised corpus.
Using this corpus, we train PARA-COMET, a discourse-aware model that
incorporates paragraph-level information to generate coherent commonsense
inferences from narratives. PARA-COMET captures both semantic knowledge
pertaining to prior world knowledge, and episodic knowledge involving how
current events relate to prior and future events in a narrative. Our results
show that PARA-COMET outperforms the sentence-level baselines, particularly in
generating inferences that are both coherent and novel.Comment: AAAI 202
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