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

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

Commonsense reasoning about social dynamics in text

Thesis (Ph.D.)--University of Washington, 2020When humans interact with each other (e.g., having conversations, sharing stories, etc.), they are able to reason more deeply about social implications in order to better understand each other and have more productive interactions. For example, when hearing someone else discuss a personal story, most people are able to think about the consequences of various events, anticipate the feelings of their conversation partner, and respond accordingly. Reasoning about social relationships in text is natural for most people, but is challenging for natural language processing models, in part because these relationships are often subtle, nuanced, and implicit. Training models for this type of inference is additionally challenging due to a lack of designated tasks, resources, and modelling frameworks specifically designed for this type of social commonsense reasoning. We approach this problem by designing new focused tasks and resources specifically aimed towards types of social reasoning. We also introduce new modeling frameworks to learn to integrate social inferences with downstream tasks such as story and dialogue generation. First, we investigate reasoning about social dynamics of characters and actions within stories. We create a new benchmark for reasoning about character mental state based on story events. We demonstrate that this type of reasoning is challenging even for state-of-the-art language understanding models. We also introduce plot dynamics as part of a new modeling framework for story generation. Our results indicate that tracking plot state and integrating discourse features are beneficial for writing tighter narratives. We also explore two types of reasoning about a speaker (e.g., a writer of a piece of text, a conversation partner, or so on) based on what they have said or written. We present connotation frames, a novel formalism for measuring connotative relationships implicit in the text that imply the writer’s underlying message. We create a connotation frames lexicon, which may be useful in tasks like detecting implied stance, bias, or subtle meaning intended by a writer. Lastly, we investigate reasoning about a speaker in the dialogue setting by exploring the challenges of creating empathetic responses to a conversation partner. We introduce the task of empathetic response generation and a new dataset for training dialogue models to generate responses that are more empathetic and socially aware of a conversation partner’s feelings