138 research outputs found
SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task
Few-shot dialogue state tracking (DST) model tracks user requests in dialogue
with reliable accuracy even with a small amount of data. In this paper, we
introduce an ontology-free few-shot DST with self-feeding belief state input.
The self-feeding belief state input increases the accuracy in multi-turn
dialogue by summarizing previous dialogue. Also, we newly developed a slot-gate
auxiliary task. This new auxiliary task helps classify whether a slot is
mentioned in the dialogue. Our model achieved the best score in a few-shot
setting for four domains on multiWOZ 2.0.Comment: Accepted in INTERSPEECH 202
DORA: Toward Policy Optimization for Task-oriented Dialogue System with Efficient Context
Recently, reinforcement learning (RL) has been applied to task-oriented
dialogue systems by using latent actions to solve shortcomings of supervised
learning (SL). In this paper, we propose a multi-domain task-oriented dialogue
system, called Dialogue System with Optimizing a Recurrent Action Policy using
Efficient Context (DORA), that uses SL, with subsequently applied RL to
optimize dialogue systems using a recurrent dialogue policy. This dialogue
policy recurrently generates explicit system actions as a both word-level and
high-level policy. As a result, DORA is clearly optimized during both SL and RL
steps by using an explicit system action policy that considers an efficient
context instead of the entire dialogue history. The system actions are both
interpretable and controllable, whereas the latent actions are not. DORA
improved the success rate by 6.6 points on MultiWOZ 2.0 and by 10.9 points on
MultiWOZ 2.1.Comment: 23 pages, 9 figures, submitted to Computer Speech ans Language
journa
Conversational QA Dataset Generation with Answer Revision
Conversational question--answer generation is a task that automatically
generates a large-scale conversational question answering dataset based on
input passages. In this paper, we introduce a novel framework that extracts
question-worthy phrases from a passage and then generates corresponding
questions considering previous conversations. In particular, our framework
revises the extracted answers after generating questions so that answers
exactly match paired questions. Experimental results show that our simple
answer revision approach leads to significant improvement in the quality of
synthetic data. Moreover, we prove that our framework can be effectively
utilized for domain adaptation of conversational question answering.Comment: COLING 202
Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring
Automated essay scoring (AES) aims to score essays written for a given
prompt, which defines the writing topic. Most existing AES systems assume to
grade essays of the same prompt as used in training and assign only a holistic
score. However, such settings conflict with real-education situations;
pre-graded essays for a particular prompt are lacking, and detailed trait
scores of sub-rubrics are required. Thus, predicting various trait scores of
unseen-prompt essays (called cross-prompt essay trait scoring) is a remaining
challenge of AES. In this paper, we propose a robust model: prompt- and trait
relation-aware cross-prompt essay trait scorer. We encode prompt-aware essay
representation by essay-prompt attention and utilizing the topic-coherence
feature extracted by the topic-modeling mechanism without access to labeled
data; therefore, our model considers the prompt adherence of an essay, even in
a cross-prompt setting. To facilitate multi-trait scoring, we design
trait-similarity loss that encapsulates the correlations of traits. Experiments
prove the efficacy of our model, showing state-of-the-art results for all
prompts and traits. Significant improvements in low-resource-prompt and
inferior traits further indicate our model's strength.Comment: Accepted at ACL 2023 (Findings, long paper
Score-balanced Loss for Multi-aspect Pronunciation Assessment
With rapid technological growth, automatic pronunciation assessment has
transitioned toward systems that evaluate pronunciation in various aspects,
such as fluency and stress. However, despite the highly imbalanced score labels
within each aspect, existing studies have rarely tackled the data imbalance
problem. In this paper, we suggest a novel loss function, score-balanced loss,
to address the problem caused by uneven data, such as bias toward the majority
scores. As a re-weighting approach, we assign higher costs when the predicted
score is of the minority class, thus, guiding the model to gain positive
feedback for sparse score prediction. Specifically, we design two weighting
factors by leveraging the concept of an effective number of samples and using
the ranks of scores. We evaluate our method on the speechocean762 dataset,
which has noticeably imbalanced scores for several aspects. Improved results
particularly on such uneven aspects prove the effectiveness of our method.Comment: Accepted at Interspeech 202
Hybrid approach to user intention modeling for dialog simulation
This paper proposes a novel user intention si-mulation method which is a data-driven ap-proach but able to integrate diverse user dis-course knowledge together to simulate various type of users. In Markov logic framework, lo-gistic regression based data-driven user inten-tion modeling is introduced, and human dialog knowledge are designed into two layers such as domain and discourse knowledge, then it is integrated with the data-driven model in gen-eration time. Cooperative, corrective and self-directing discourse knowledge are designed and integrated to mimic such type of users. Experiments were carried out to investigate the patterns of simulated users, and it turned out that our approach was successful to gener-ate user intention patterns which are not only unseen in the training corpus and but also per-sonalized in the designed direction.
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