145 research outputs found
Modeling Empathy and Distress in Reaction to News Stories
Computational detection and understanding of empathy is an important factor
in advancing human-computer interaction. Yet to date, text-based empathy
prediction has the following major limitations: It underestimates the
psychological complexity of the phenomenon, adheres to a weak notion of ground
truth where empathic states are ascribed by third parties, and lacks a shared
corpus. In contrast, this contribution presents the first publicly available
gold standard for empathy prediction. It is constructed using a novel
annotation methodology which reliably captures empathy assessments by the
writer of a statement using multi-item scales. This is also the first
computational work distinguishing between multiple forms of empathy, empathic
concern, and personal distress, as recognized throughout psychology. Finally,
we present experimental results for three different predictive models, of which
a CNN performs the best.Comment: To appear at EMNLP 201
A Unified Framework for Slot based Response Generation in a Multimodal Dialogue System
Natural Language Understanding (NLU) and Natural Language Generation (NLG)
are the two critical components of every conversational system that handles the
task of understanding the user by capturing the necessary information in the
form of slots and generating an appropriate response in accordance with the
extracted information. Recently, dialogue systems integrated with complementary
information such as images, audio, or video have gained immense popularity. In
this work, we propose an end-to-end framework with the capability to extract
necessary slot values from the utterance and generate a coherent response,
thereby assisting the user to achieve their desired goals in a multimodal
dialogue system having both textual and visual information. The task of
extracting the necessary information is dependent not only on the text but also
on the visual cues present in the dialogue. Similarly, for the generation, the
previous dialog context comprising multimodal information is significant for
providing coherent and informative responses. We employ a multimodal
hierarchical encoder using pre-trained DialoGPT and also exploit the knowledge
base (Kb) to provide a stronger context for both the tasks. Finally, we design
a slot attention mechanism to focus on the necessary information in a given
utterance. Lastly, a decoder generates the corresponding response for the given
dialogue context and the extracted slot values. Experimental results on the
Multimodal Dialogue Dataset (MMD) show that the proposed framework outperforms
the baselines approaches in both the tasks. The code is available at
https://github.com/avinashsai/slot-gpt.Comment: Published in the journal Multimedia Tools and Application
DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
Interpersonal language style shifting in dialogues is an interesting and
almost instinctive ability of human. Understanding interpersonal relationship
from language content is also a crucial step toward further understanding
dialogues. Previous work mainly focuses on relation extraction between named
entities in texts. In this paper, we propose the task of relation
classification of interlocutors based on their dialogues. We crawled movie
scripts from IMSDb, and annotated the relation labels for each session
according to 13 pre-defined relationships. The annotated dataset DDRel consists
of 6300 dyadic dialogue sessions between 694 pair of speakers with 53,126
utterances in total. We also construct session-level and pair-level relation
classification tasks with widely-accepted baselines. The experimental results
show that this task is challenging for existing models and the dataset will be
useful for future research.Comment: This paper has been accepted by AAAI202
One-Shot Labeling for Automatic Relevance Estimation
Dealing with unjudged documents ("holes") in relevance assessments is a
perennial problem when evaluating search systems with offline experiments.
Holes can reduce the apparent effectiveness of retrieval systems during
evaluation and introduce biases in models trained with incomplete data. In this
work, we explore whether large language models can help us fill such holes to
improve offline evaluations. We examine an extreme, albeit common, evaluation
setting wherein only a single known relevant document per query is available
for evaluation. We then explore various approaches for predicting the relevance
of unjudged documents with respect to a query and the known relevant document,
including nearest neighbor, supervised, and prompting techniques. We find that
although the predictions of these One-Shot Labelers (1SL) frequently disagree
with human assessments, the labels they produce yield a far more reliable
ranking of systems than the single labels do alone. Specifically, the strongest
approaches can consistently reach system ranking correlations of over 0.86 with
the full rankings over a variety of measures. Meanwhile, the approach
substantially increases the reliability of t-tests due to filling holes in
relevance assessments, giving researchers more confidence in results they find
to be significant. Alongside this work, we release an easy-to-use software
package to enable the use of 1SL for evaluation of other ad-hoc collections or
systems.Comment: SIGIR 202
Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding
Contrastive learning has become a new paradigm for unsupervised sentence
embeddings. Previous studies focus on instance-wise contrastive learning,
attempting to construct positive pairs with textual data augmentation. In this
paper, we propose a novel Contrastive learning method with Prompt-derived
Virtual semantic Prototypes (ConPVP). Specifically, with the help of prompts,
we construct virtual semantic prototypes to each instance, and derive negative
prototypes by using the negative form of the prompts. Using a prototypical
contrastive loss, we enforce the anchor sentence embedding to be close to its
corresponding semantic prototypes, and far apart from the negative prototypes
as well as the prototypes of other sentences. Extensive experimental results on
semantic textual similarity, transfer, and clustering tasks demonstrate the
effectiveness of our proposed model compared to strong baselines. Code is
available at https://github.com/lemon0830/promptCSE.Comment: Findings of EMNLP 202
Multimodal Automated Fact-Checking: A Survey
Misinformation is often conveyed in multiple modalities, e.g. a miscaptioned
image. Multimodal misinformation is perceived as more credible by humans, and
spreads faster than its text-only counterparts. While an increasing body of
research investigates automated fact-checking (AFC), previous surveys mostly
focus on text. In this survey, we conceptualise a framework for AFC including
subtasks unique to multimodal misinformation. Furthermore, we discuss related
terms used in different communities and map them to our framework. We focus on
four modalities prevalent in real-world fact-checking: text, image, audio, and
video. We survey benchmarks and models, and discuss limitations and promising
directions for future researchComment: The 2023 Conference on Empirical Methods in Natural Language
Processing (EMNLP): Finding
Generative Knowledge Graph Construction: A Review
Generative Knowledge Graph Construction (KGC) refers to those methods that
leverage the sequence-to-sequence framework for building knowledge graphs,
which is flexible and can be adapted to widespread tasks. In this study, we
summarize the recent compelling progress in generative knowledge graph
construction. We present the advantages and weaknesses of each paradigm in
terms of different generation targets and provide theoretical insight and
empirical analysis. Based on the review, we suggest promising research
directions for the future. Our contributions are threefold: (1) We present a
detailed, complete taxonomy for the generative KGC methods; (2) We provide a
theoretical and empirical analysis of the generative KGC methods; (3) We
propose several research directions that can be developed in the future.Comment: Accepted to EMNLP 2022 (oral) and a public repository is available in
https://github.com/zjunlp/Generative_KG_Construction_Paper
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