187 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 parallel corpus of Python functions and documentation strings for automated code documentation and code generation
Automated documentation of programming source code and automated code
generation from natural language are challenging tasks of both practical and
scientific interest. Progress in these areas has been limited by the low
availability of parallel corpora of code and natural language descriptions,
which tend to be small and constrained to specific domains.
In this work we introduce a large and diverse parallel corpus of a hundred
thousands Python functions with their documentation strings ("docstrings")
generated by scraping open source repositories on GitHub. We describe baseline
results for the code documentation and code generation tasks obtained by neural
machine translation. We also experiment with data augmentation techniques to
further increase the amount of training data.
We release our datasets and processing scripts in order to stimulate research
in these areas.Comment: 5 pages, 1 figure, 3 table
LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Search
Queries with similar information needs tend to have similar document clicks,
especially in biomedical literature search engines where queries are generally
short and top documents account for most of the total clicks. Motivated by
this, we present a novel architecture for biomedical literature search, namely
Log-Augmented DEnse Retrieval (LADER), which is a simple plug-in module that
augments a dense retriever with the click logs retrieved from similar training
queries. Specifically, LADER finds both similar documents and queries to the
given query by a dense retriever. Then, LADER scores relevant (clicked)
documents of similar queries weighted by their similarity to the input query.
The final document scores by LADER are the average of (1) the document
similarity scores from the dense retriever and (2) the aggregated document
scores from the click logs of similar queries. Despite its simplicity, LADER
achieves new state-of-the-art (SOTA) performance on TripClick, a recently
released benchmark for biomedical literature retrieval. On the frequent (HEAD)
queries, LADER largely outperforms the best retrieval model by 39% relative
NDCG@10 (0.338 v.s. 0.243). LADER also achieves better performance on the less
frequent (TORSO) queries with 11% relative NDCG@10 improvement over the
previous SOTA (0.303 v.s. 0.272). On the rare (TAIL) queries where similar
queries are scarce, LADER still compares favorably to the previous SOTA method
(NDCG@10: 0.310 v.s. 0.295). On all queries, LADER can improve the performance
of a dense retriever by 24%-37% relative NDCG@10 while not requiring additional
training, and further performance improvement is expected from more logs. Our
regression analysis has shown that queries that are more frequent, have higher
entropy of query similarity and lower entropy of document similarity, tend to
benefit more from log augmentation.Comment: SIGIR 202
Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning
Pre-trained language models have contributed significantly to relation
extraction by demonstrating remarkable few-shot learning abilities. However,
prompt tuning methods for relation extraction may still fail to generalize to
those rare or hard patterns. Note that the previous parametric learning
paradigm can be viewed as memorization regarding training data as a book and
inference as the close-book test. Those long-tailed or hard patterns can hardly
be memorized in parameters given few-shot instances. To this end, we regard RE
as an open-book examination and propose a new semiparametric paradigm of
retrieval-enhanced prompt tuning for relation extraction. We construct an
open-book datastore for retrieval regarding prompt-based instance
representations and corresponding relation labels as memorized key-value pairs.
During inference, the model can infer relations by linearly interpolating the
base output of PLM with the non-parametric nearest neighbor distribution over
the datastore. In this way, our model not only infers relation through
knowledge stored in the weights during training but also assists
decision-making by unwinding and querying examples in the open-book datastore.
Extensive experiments on benchmark datasets show that our method can achieve
state-of-the-art in both standard supervised and few-shot settings. Code are
available in https://github.com/zjunlp/PromptKG/tree/main/research/RetrievalRE.Comment: Accepted by SIGIR 2022, short pape
An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing
In this work, we explore multiple neural architectures adapted for the task
of automatic post-editing of machine translation output. We focus on neural
end-to-end models that combine both inputs (raw MT output) and
(source language input) in a single neural architecture, modeling directly. Apart from that, we investigate the influence of
hard-attention models which seem to be well-suited for monolingual tasks, as
well as combinations of both ideas. We report results on data sets provided
during the WMT-2016 shared task on automatic post-editing and can demonstrate
that dual-attention models that incorporate all available data in the APE
scenario in a single model improve on the best shared task system and on all
other published results after the shared task. Dual-attention models that are
combined with hard attention remain competitive despite applying fewer changes
to the input.Comment: Accepted for presentation at IJCNLP 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
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