Dominican Republic

In this paper, we study the possibility of unsupervised Multiple Choices Question Answering (MCQA). From very basic knowledge, the MCQA model knows that some choices have higher probabilities of being correct than others. The information, though very noisy, guides the training of an MCQA model. The proposed method is shown to outperform the baseline approaches on RACE and is even comparable with some supervised learning approaches on MC500.

EMNLP 2021

Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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Registered attendees will receive access the week of November 1st.

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

workshop paper

Neural dialog models are known to suffer from problems such as generating unsafe and inconsistent responses. Even though these problems are crucial and prevalent, they are mostly manually identified by model designers through interactions. Recently, some research instructs crowdworkers to goad the bots into triggering such problems. However, humans leverage superficial clues such as hate speech, while leaving systematic problems undercover. In this paper, we propose two methods including reinforcement learning to automatically trigger a dialog model into generating problematic responses. We show the effect of our methods in exposing safety and contradiction issues with state-of-the-art dialog models.

Automatically Exposing Problems with Neural Dialog Models

Next generation task-oriented dialog systems need to understand conversational contexts with their perceived surroundings, to effectively help users in the real-world multimodal environment. Existing task-oriented dialog datasets aimed towards virtual assistance fall short and do not situate the dialog in the user's multimodal context. To overcome, we present a new dataset for Situated and Interactive Multimodal Conversations, SIMMC 2.0, which includes 11K task-oriented user<>assistant dialogs (117K utterances) in the shopping domain, grounded in immersive and photo-realistic scenes. The dialogs are collected using a two-phase pipeline: (1) A novel multimodal dialog simulator generates simulated dialog flows, with an emphasis on diversity and richness of interactions, (2) Manual paraphrasing of the generated utterances to collect diverse referring expressions. We provide an in-depth analysis of the collected dataset, and describe in detail the four main benchmark tasks we propose. Our baseline model, powered by the state-of-the-art language model, shows promising results, and highlights new challenges and directions for the community to study. Code & data are made publicly available: https://github.com/facebookresearch/simmc2.

SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations

An ideal integration of autonomous agents in a human world implies that they are able to collaborate on human terms. In particular, theory of mind plays an important role in maintaining common ground during human collaboration and communication. To enable theory of mind modeling in situated interactions, we introduce a fine-grained dataset of collaborative tasks performed by pairs of human subjects in the 3D virtual blocks world of Minecraft. It provides information that captures partners' beliefs of the world and of each other as an interaction unfolds, bringing abundant opportunities to study human collaborative behaviors in situated language communication. As a first step towards our goal of developing embodied AI agents able to infer belief states of collaborative partners in situ, we build and present results on computational models for several theory of mind tasks.

MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks

In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. We present a simple yet effective few-shot intent detection schema via contrastive pre-training and fine-tuning. Specifically, we first conduct self-supervised contrastive pre-training on collected intent datasets, which implicitly learns to discriminate semantically similar utterances without using any labels. We then perform few-shot intent detection together with supervised contrastive learning, which explicitly pulls utterances from the same intent closer and pushes utterances across different intents farther. Experimental results show that our proposed method achieves state-of-the-art performance on three challenging intent detection datasets under 5-shot and 10-shot settings.

Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning

Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation. We introduce a knowledge identification model that leverages the document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. An auxiliary loss captures the history of dialogue-document connections. We demonstrate the effectiveness of our model on two document-grounded conversational datasets and provide analyses showing generalization to unseen documents and long dialogue contexts.

DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization

Humans often employ figurative language use in communication, including during interactions with dialog systems. Thus, it is important for real-world dialog systems to be able to handle popular figurative language constructs like metaphor and simile. In this work, we analyze the performance of existing dialog models in situations where the input dialog context exhibits use of figurative language. We observe large gaps in handling of figurative language when evaluating the models on two open domain dialog datasets. When faced with dialog contexts consisting of figurative language, some models show very large drops in performance compared to contexts without figurative language. We encourage future research in dialog modeling to separately analyze and report results on figurative language in order to better test model capabilities relevant to real-world use. Finally, we propose lightweight solutions to help existing models become more robust to figurative language by simply using an external resource to translate figurative language to literal (non-figurative) forms while preserving the meaning to the best extent possible.

Investigating Robustness of Dialog Models to Popular Figurative Language Constructs

Chatbots in healthcare can be used to collect information about the user with different purposes: treatment adherence, monitoring, patient support program, patient education, behavior change, diagnosis.
Considering monitoring, the task of filling clinical questionnaires (to measure quality of life for e.g.) in an automated way from a patient-machine dialogue received less attention. Although, it opens the opportunity to get updated patient's information more regularly for a closer monitoring.
The goal of this research is to study the task of automatically filling medical questionnaires based on conversation histories.
Due to the novelty of the task, lack of a relevant dataset and the difficulties of collecting data, we investigate the capabilities of state-of-the-art zero-shot models.

Zero-Shot Clinical Questionnaire Filling From Human-Machine Interactions

Question answering (QA) models for reading comprehension have been demonstrated to exploit unintended dataset biases such as question--context lexical overlap.
This hinders QA models from generalizing to under-represented samples such as questions with low lexical overlap.
Question generation (QG), a method for augmenting QA datasets, can be a solution for such performance degradation if QG can properly debias QA datasets.
However, we discover that recent neural QG models are biased towards generating questions with high lexical overlap, which can amplify the dataset bias.
Moreover, our analysis reveals that data augmentation with these QG models frequently impairs the performance on questions with low lexical overlap, while improving that on questions with high lexical overlap.
To address this problem, we use a synonym replacement-based approach to augment questions with low lexical overlap.
We demonstrate that the proposed data augmentation approach is simple yet effective to mitigate the degradation problem with only 70k synthetic examples.

Can Question Generation Debias Question Answering Models? A Case Study on Question–Context Lexical Overlap

Given a text passage as prompt (input), generative language models (GLMs)
predict the text to follow that prompt. When the prompt contains a passage
followed by a question about that passage, then we ask, how well does the text predicted by a GLM answer it?

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Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge

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