Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMultitasking Inhibits Semantic Drift
تعدد المهام يمنع الانجراف الدلالي
437
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
عندما يتواصل الوكلاء الذكيون بإنجاز الأهداف المشتركة، كيف تشكل هذه الأهداف لغة الوكلاء؟ندرس ديناميات التعلم في سياسات اللغة الكامنة (LTPs)، حيث يولد وكلاء المعلمون الأوصاف الفرعية باللغة الطبيعية ووكلاء المنفذين تعيين هذه الأوصاف إلى إجراءات منخفضة المستوى.يمكن LLP حل مشاكل تعليم التعزيز الطويلة في الأفق وتقديم نموذج غني لدراسة استخدام اللغة الموجهة نحو المهام.لكن العمل السابق قد وجد أن التدريب LLP عرضة للانجراف الدلالي (استخدام الرسائل بطرق غير متناسقة مع معاني اللغة الطبيعية الأصلية).هنا، نوضح نظري وتجريبيا أن التدريب المتعدد هو مضاد فعال لهذه المشكلة: نثبت أن التدريب المتعدد يتزيل الانجراف الدلالي في عائلة مدرسية جيدا من ألعاب الإشارات، وإظهار أن التدريب المتعدد في LT LT LTWural في لعبة استراتيجية معقدة تقللالانجراف وبين تحسين كفاءة عينة.
When intelligent agents communicate to accomplish shared goals, how do these goals shape the agents' language? We study the dynamics of learning in latent language policies (LLPs), in which instructor agents generate natural-language subgoal descriptions and executor agents map these descriptions to low-level actions. LLPs can solve challenging long-horizon reinforcement learning problems and provide a rich model for studying task-oriented language use. But previous work has found that LLP training is prone to semantic drift (use of messages in ways inconsistent with their original natural language meanings). Here, we demonstrate theoretically and empirically that multitask training is an effective counter to this problem: we prove that multitask training eliminates semantic drift in a well-studied family of signaling games, and show that multitask training of neural LLPs in a complex strategy game reduces drift and while improving sample efficiency.
References used
https://aclanthology.org/
rate research
Read More
The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders. In this work, we explore an alternative paradigm. We formulate semantic
parsing as a dependency parsing task, applying graph-based decoding techniques developed for syntactic parsing. We compare various decoding techniques given the same pre-trained Transformer encoder on the TOP dataset, including settings where training data is limited or contains only partially-annotated examples. We find that our graph-based approach is competitive with sequence decoders on the standard setting, and offers significant improvements in data efficiency and settings where partially-annotated data is available.
Performance of neural models for named entity recognition degrades over time, becoming stale. This degradation is due to temporal drift, the change in our target variables' statistical properties over time. This issue is especially problematic for so
cial media data, where topics change rapidly. In order to mitigate the problem, data annotation and retraining of models is common. Despite its usefulness, this process is expensive and time-consuming, which motivates new research on efficient model updating. In this paper, we propose an intuitive approach to measure the potential trendiness of tweets and use this metric to select the most informative instances to use for training. We conduct experiments on three state-of-the-art models on the Temporal Twitter Dataset. Our approach shows larger increases in prediction accuracy with less training data than the alternatives, making it an attractive, practical solution.
This paper introduces Semantic Frame Forecast, a task that predicts the semantic frames that will occur in the next 10, 100, or even 1,000 sentences in a running story. Prior work focused on predicting the immediate future of a story, such as one to
a few sentences ahead. However, when novelists write long stories, generating a few sentences is not enough to help them gain high-level insight to develop the follow-up story. In this paper, we formulate a long story as a sequence of story blocks,'' where each block contains a fixed number of sentences (e.g., 10, 100, or 200). This formulation allows us to predict the follow-up story arc beyond the scope of a few sentences. We represent a story block using the term frequencies (TF) of semantic frames in it, normalized by each frame's inverse document frequency (IDF). We conduct semantic frame forecast experiments on 4,794 books from the Bookcorpus and 7,962 scientific abstracts from CODA-19, with block sizes ranging from 5 to 1,000 sentences. The results show that automated models can forecast the follow-up story blocks better than the random, prior, and replay baselines, indicating the feasibility of the task. We also learn that the models using the frame representation as features outperform all the existing approaches when the block size is over 150 sentences. The human evaluation also shows that the proposed frame representation, when visualized as word clouds, is comprehensible, representative, and specific to humans.
In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence. We hypothesize that a unified lexical semantic recognition task is an effective way to encapsu
late previously disparate styles of annotation, including multiword expression identification / classification and supersense tagging. Using the STREUSLE corpus, we train a neural CRF sequence tagger and evaluate its performance along various axes of annotation. As the label set generalizes that of previous tasks (PARSEME, DiMSUM), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes baseline models and evaluation metrics for integrated and accurate modeling of lexical semantics, facilitating future work in this area.
The amount of information available online can be overwhelming for users to digest, specially when dealing with other users' comments when making a decision about buying a product or service. In this context, opinion summarization systems are of grea
t value, extracting important information from the texts and presenting them to the user in a more understandable manner. It is also known that the usage of semantic representations can benefit the quality of the generated summaries. This paper aims at developing opinion summarization methods based on Abstract Meaning Representation of texts in the Brazilian Portuguese language. Four different methods have been investigated, alongside some literature approaches. The results show that a Machine Learning-based method produced summaries of higher quality, outperforming other literature techniques on manually constructed semantic graphs. We also show that using parsed graphs over manually annotated ones harmed the output. Finally, an analysis of how important different types of information are for the summarization process suggests that using Sentiment Analysis features did not improve summary quality.
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
