19 research outputs found
Connotation Frames: A Data-Driven Investigation
Through a particular choice of a predicate (e.g., "x violated y"), a writer
can subtly connote a range of implied sentiments and presupposed facts about
the entities x and y: (1) writer's perspective: projecting x as an
"antagonist"and y as a "victim", (2) entities' perspective: y probably dislikes
x, (3) effect: something bad happened to y, (4) value: y is something valuable,
and (5) mental state: y is distressed by the event. We introduce connotation
frames as a representation formalism to organize these rich dimensions of
connotation using typed relations. First, we investigate the feasibility of
obtaining connotative labels through crowdsourcing experiments. We then present
models for predicting the connotation frames of verb predicates based on their
distributional word representations and the interplay between different types
of connotative relations. Empirical results confirm that connotation frames can
be induced from various data sources that reflect how people use language and
give rise to the connotative meanings. We conclude with analytical results that
show the potential use of connotation frames for analyzing subtle biases in
online news media.Comment: 11 pages, published in Proceedings of ACL 201
Investigating Content Planning for Navigating Trade-offs in Knowledge-Grounded Dialogue
Knowledge-grounded dialogue generation is a challenging task because it
requires satisfying two fundamental yet often competing constraints: being
responsive in a manner that is specific to what the conversation partner has
said while also being attributable to an underlying source document. In this
work, we bring this trade-off between these two objectives (specificity and
attribution) to light and ask the question: Can explicit content planning
before the response generation help the model to address this challenge? To
answer this question, we design a framework called PLEDGE, which allows us to
experiment with various plan variables explored in prior work, supporting both
metric-agnostic and metric-aware approaches. While content planning shows
promise, our results on whether it can actually help to navigate this trade-off
are mixed -- planning mechanisms that are metric-aware (use automatic metrics
during training) are better at automatic evaluations but underperform in human
judgment compared to metric-agnostic mechanisms. We discuss how this may be
caused by over-fitting to automatic metrics and the need for future work to
better calibrate these metrics towards human judgment. We hope the observations
from our analysis will inform future work that aims to apply content planning
in this context.Comment: Accepted at EACL 2024 Main Conference (Long
ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
We present ATOMIC, an atlas of everyday commonsense reasoning, organized
through 877k textual descriptions of inferential knowledge. Compared to
existing resources that center around taxonomic knowledge, ATOMIC focuses on
inferential knowledge organized as typed if-then relations with variables
(e.g., "if X pays Y a compliment, then Y will likely return the compliment").
We propose nine if-then relation types to distinguish causes vs. effects,
agents vs. themes, voluntary vs. involuntary events, and actions vs. mental
states. By generatively training on the rich inferential knowledge described in
ATOMIC, we show that neural models can acquire simple commonsense capabilities
and reason about previously unseen events. Experimental results demonstrate
that multitask models that incorporate the hierarchical structure of if-then
relation types lead to more accurate inference compared to models trained in
isolation, as measured by both automatic and human evaluation.Comment: AAAI 2019 C
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Compared to standard retrieval tasks, passage retrieval for conversational
question answering (CQA) poses new challenges in understanding the current user
question, as each question needs to be interpreted within the dialogue context.
Moreover, it can be expensive to re-train well-established retrievers such as
search engines that are originally developed for non-conversational queries. To
facilitate their use, we develop a query rewriting model CONQRR that rewrites a
conversational question in the context into a standalone question. It is
trained with a novel reward function to directly optimize towards retrieval
using reinforcement learning and can be adapted to any off-the-shelf retriever.
We show that CONQRR achieves state-of-the-art results on a recent open-domain
CQA dataset containing conversations from three different sources, and is
effective for two different off-the-shelf retrievers. Our extensive analysis
also shows the robustness of CONQRR to out-of-domain dialogues as well as to
zero query rewriting supervision