Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTelling Stories through Multi-User Dialogue by Modeling Character Relations
سرد القصص من خلال حوار متعدد المستخدمين عن طريق نمذجة العلاقات الشخصية
106
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تستكشف هذه الورقة استمرار القصة التي يحركها الشخصية، حيث تظهر القصة من خلال سرد الشخصيات الأول والثاني بالإضافة إلى الحوار - - - - تتطلب النماذج لتحديد اللغة التي تتفق مع شخصيات الشخصية وعلاقاتها مع أحرف أخرى التالية وتقدم القصة. نحن نفترض أن نموذج متعدد المهام الذي يتدرب على حوار الأحرف بالإضافة إلى معلومات علاقة الشخصية يحسن استمرار القصة المستندة إلى المحولات. تحقيقا لهذه الغاية، نقوم بتوسيع دور محصنة الدوران والتنين الحاسم (Rameshkumar و Bailey، 2020) --- تتكون من نصوص الحوار من الأشخاص الذين يخبرون بشكل تعاظم قصة أثناء لعب لعبة الأبراج المحصنة والتنينات --- مع العلاقات المستخرجة تلقائيا بين كل زوج من الشخصيات التفاعل وكذلك شخصياتهم. تقدم سلسلة من الوضوح دليلا على فرضيتنا، حيث أظهر أن نموذجنا متعدد المهام لدينا باستخدام علاقات الأحرف يحسن دقة استمرار القصة على خطوط خطوط خطوط خطوط خطوط طويلة.
This paper explores character-driven story continuation, in which the story emerges through characters' first- and second-person narration as well as dialogue---requiring models to select language that is consistent with a character's persona and their relationships with other characters while following and advancing the story. We hypothesize that a multi-task model that trains on character dialogue plus character relationship information improves transformer-based story continuation. To this end, we extend the Critical Role Dungeons and Dragons Dataset (Rameshkumar and Bailey, 2020)---consisting of dialogue transcripts of people collaboratively telling a story while playing the role-playing game Dungeons and Dragons---with automatically extracted relationships between each pair of interacting characters as well as their personas. A series of ablations lend evidence to our hypothesis, showing that our multi-task model using character relationships improves story continuation accuracy over strong baselines.
References used
https://aclanthology.org/
rate research
Read More
Dialogue state tracking models play an important role in a task-oriented dialogue system. However, most of them model the slot types conditionally independently given the input. We discover that it may cause the model to be confused by slot types tha
t share the same data type. To mitigate this issue, we propose TripPy-MRF and TripPy-LSTM that models the slots jointly. Our results show that they are able to alleviate the confusion mentioned above, and they push the state-of-the-art on dataset MultiWoz 2.1 from 58.7 to 61.3.
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 dialo
gue-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.
With the rapid increase in the volume of dialogue data from daily life, there is a growing demand for dialogue summarization. Unfortunately, training a large summarization model is generally infeasible due to the inadequacy of dialogue data with anno
tated summaries. Most existing works for low-resource dialogue summarization directly pretrain models in other domains, e.g., the news domain, but they generally neglect the huge difference between dialogues and conventional articles. To bridge the gap between out-of-domain pretraining and in-domain fine-tuning, in this work, we propose a multi-source pretraining paradigm to better leverage the external summary data. Specifically, we exploit large-scale in-domain non-summary data to separately pretrain the dialogue encoder and the summary decoder. The combined encoder-decoder model is then pretrained on the out-of-domain summary data using adversarial critics, aiming to facilitate domain-agnostic summarization. The experimental results on two public datasets show that with only limited training data, our approach achieves competitive performance and generalizes well in different dialogue scenarios.
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity in short-text or small collection of documents. This work presents a
novel neural topic modeling framework using multi-view embed ding spaces: (1) pretrained topic-embeddings, and (2) pretrained word-embeddings (context-insensitive from Glove and context-sensitive from BERT models) jointly from one or many sources to improve topic quality and better deal with polysemy. In doing so, we first build respective pools of pretrained topic (i.e., TopicPool) and word embeddings (i.e., WordPool). We then identify one or more relevant source domain(s) and transfer knowledge to guide meaningful learning in the sparse target domain. Within neural topic modeling, we quantify the quality of topics and document representations via generalization (perplexity), interpretability (topic coherence) and information retrieval (IR) using short-text, long-text, small and large document collections from news and medical domains. Introducing the multi-source multi-view embedding spaces, we have shown state-of-the-art neural topic modeling using 6 source (high-resource) and 5 target (low-resource) corpora.
Production NMT systems typically need to serve niche domains that are not covered by adequately large and readily available parallel corpora. As a result, practitioners often fine-tune general purpose models to each of the domains their organisation
caters to. The number of domains however can often become large, which in combination with the number of languages that need serving can lead to an unscalable fleet of models to be developed and maintained. We propose Multi Dimensional Tagging, a method for fine-tuning a single NMT model on several domains simultaneously, thus drastically reducing development and maintenance costs. We run experiments where a single MDT model compares favourably to a set of SOTA specialist models, even when evaluated on the domain those baselines have been fine-tuned on. Besides BLEU, we report human evaluation results. MDT models are now live at Booking.com, powering an MT engine that serves millions of translations a day in over 40 different languages.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
