11 research outputs found
Diversity Enhanced Narrative Question Generation for Storybooks
Question generation (QG) from a given context can enhance comprehension,
engagement, assessment, and overall efficacy in learning or conversational
environments. Despite recent advancements in QG, the challenge of enhancing or
measuring the diversity of generated questions often remains unaddressed. In
this paper, we introduce a multi-question generation model (mQG), which is
capable of generating multiple, diverse, and answerable questions by focusing
on context and questions. To validate the answerability of the generated
questions, we employ a SQuAD2.0 fine-tuned question answering model,
classifying the questions as answerable or not. We train and evaluate mQG on
the FairytaleQA dataset, a well-structured QA dataset based on storybooks, with
narrative questions. We further apply a zero-shot adaptation on the TellMeWhy
and SQuAD1.1 datasets. mQG shows promising results across various evaluation
metrics, among strong baselines.Comment: Accepted to EMNLP 202
PEMA: Plug-in External Memory Adaptation for Language Models
Pre-trained language models (PLMs) have demonstrated impressive performance
across various downstream NLP tasks. Nevertheless, the resource requirements of
pre-training large language models in terms of memory and training compute pose
significant challenges. Furthermore, due to the substantial resources required,
many PLM weights are confidential. Consequently, users are compelled to share
their data with model owners for fine-tuning on specific tasks. To overcome the
limitations, we introduce Plug-in External Memory Adaptation (PEMA), a
Parameter-Efficient Fine-Tuning (PEFT) approach designed for fine-tuning PLMs
without the need for all weights. PEMA can be integrated into the context
representation of test data during inference to execute downstream tasks. It
leverages an external memory to store context representations generated by a
PLM, mapped with the desired target word. Our method entails training
LoRA-based weight matrices within the final layer of the PLM for enhanced
efficiency. The probability is then interpolated with the next-word
distribution from the PLM to perform downstream tasks. To improve the
generation quality, we propose a novel interpolation strategy named Gradual
Unrolling. To demonstrate the effectiveness of our proposed method, we conduct
experiments to demonstrate the efficacy of PEMA with a syntactic dataset and
assess its performance on machine translation and style transfer tasks using
real datasets. PEMA outperforms other PEFT methods in terms of memory and
latency efficiency for training and inference. Furthermore, it outperforms
other baselines in preserving the meaning of sentences while generating
appropriate language and styles
From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models
Being able to predict people's opinions on issues and behaviors in realistic
scenarios can be helpful in various domains, such as politics and marketing.
However, conducting large-scale surveys like the European Social Survey to
solicit people's opinions on individual issues can incur prohibitive costs.
Leveraging prior research showing influence of core human values on individual
decisions and actions, we propose to use value-injected large language models
(LLM) to predict opinions and behaviors. To this end, we present Value
Injection Method (VIM), a collection of two methods -- argument generation and
question answering -- designed to inject targeted value distributions into LLMs
via fine-tuning. We then conduct a series of experiments on four tasks to test
the effectiveness of VIM and the possibility of using value-injected LLMs to
predict opinions and behaviors of people. We find that LLMs value-injected with
variations of VIM substantially outperform the baselines. Also, the results
suggest that opinions and behaviors can be better predicted using
value-injected LLMs than the baseline approaches.Comment: EMNLP 2023 main paper accepte
Translating Hanja Historical Documents to Contemporary Korean and English
The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of
Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals
were originally written in an archaic Korean writing system, `Hanja', and were
translated into Korean from 1968 to 1993. The resulting translation was however
too literal and contained many archaic Korean words; thus, a new expert
translation effort began in 2012. Since then, the records of only one king have
been completed in a decade. In parallel, expert translators are working on
English translation, also at a slow pace and produced only one king's records
in English so far. Thus, we propose H2KE, a neural machine translation model,
that translates historical documents in Hanja to more easily understandable
Korean and to English. Built on top of multilingual neural machine translation,
H2KE learns to translate a historical document written in Hanja, from both a
full dataset of outdated Korean translation and a small dataset of more
recently translated contemporary Korean and English. We compare our method
against two baselines: a recent model that simultaneously learns to restore and
translate Hanja historical document and a Transformer based model trained only
on newly translated corpora. The experiments reveal that our method
significantly outperforms the baselines in terms of BLEU scores for both
contemporary Korean and English translations. We further conduct extensive
human evaluation which shows that our translation is preferred over the
original expert translations by both experts and non-expert Korean speakers.Comment: 2022 EMNLP Finding
Do You Feel What I Feel? Social Aspects of Emotions in Twitter Conversations
We present a computational framework for understanding the social aspects of emotions in Twitter conversations. Using unannotated data and semisupervised machine learning, we look at emotional transitions, emotional influences among the conversation partners, and patterns in the overall emotional exchanges. We find that conversational partners usually express the same emotion, which we name Emotion accommodation, but when they do not, one of the conversational partners tends to respond with a positive emotion. We also show that tweets containing sympathy, apology, and complaint are significant emotion influencers. We verify the emotion classification part of our framework by a human-annotated corpus