Huggingface hub link to play with the model: prithviraj-maurya/alexa_converting_pov
This paper delves into the intricate realm of point-of-view (POV) conversion in voice messages within the landscape of virtual assistants (VAs). VAs, such as Amazon Alexa and Google Assistant, play multifaceted roles, from executing tasks to facilitating interpersonal communication. The paper addresses the challenge of adapting the perspective of messages for diverse contexts and recipients, exploring rule-based approaches, sequence-to-sequence models, and advanced Transformers. The rule-based approach demonstrates reasonable performance, while more sophisticated models exhibit higher accuracy. The paper concludes by envisioning future enhancements, including quotation detection and paraphrase generation, to further refine the role of VAs as conversational intermediaries.
Author: Prithviraj Anil Maurya
In the vast landscape of virtual assistants (VAs), such as Amazon Alexa and Google Assistant, cloud-based software systems are meticulously designed to decipher spoken utterances, comprehend user intents, and seamlessly execute requested actions. Virtual assistants serve a myriad of functions, ranging from playing music, setting timers, and providing encyclopedic information to controlling smart home devices. Moreover, they facilitate user-to-user communication, enabling voice calls and text messaging.
This paper zooms in on a specific aspect of VA interactions -- the conversion of the point of view in messages. When users communicate messages to VAs, there often arises a need to adapt the perspective of these messages to suit different contexts and recipients. This task becomes particularly challenging when dealing with both direct and indirect messages, necessitating intricate natural language processing to handle co-reference relations effectively. Furthermore, when VAs function as intermediaries, the point of view conversion becomes essential for maintaining the natural flow of human-robot interactions. This paper delves into these challenges and explores various approaches to address them, ultimately aiming to enhance the role of VAs as intermediaries in conversational exchanges.
How can the point-of-view (POV) in voice messages within the context of virtual assistants be effectively converted to suit diverse contexts and recipients, and what approaches yield optimal results in this challenging natural language processing task?
In the evolving landscape of virtual assistants (VAs), such as Amazon Alexa and Google Assistant, sophisticated cloud-based systems are designed to interpret spoken language, discern user intentions, and execute a myriad of requested actions. These VAs play multifaceted roles, from simple tasks like playing music and setting reminders to more complex functions like controlling smart home devices and facilitating user-to-user communication through voice calls and text messaging.
One particular aspect of VA interactions that presents a unique set of challenges is the conversion of the point of view (POV) in messages. When users communicate with VAs, adapting the perspective of messages becomes essential for diverse contexts and recipients. This complexity is particularly evident when dealing with both direct and indirect messages, requiring nuanced natural language processing (NLP) techniques to handle co-reference relations effectively.
Moreover, as VAs increasingly function as intermediaries in human-robot interactions, the accurate conversion of POV becomes crucial for maintaining the natural flow of conversations. In this paper, we delve into the intricacies of this problem, exploring various approaches, from rule-based methods to advanced Transformer models, to enhance the capability of VAs as intermediaries in conversational exchanges.
The dataset utilized for this research underwent meticulous collection and verification through a collaborative effort involving Amazon Mechanical Turk workers (Callison-Burch and Dredze, 2010) and internal associates. The dataset was anonymized by systematically replacing names with special tokens, such as "@CN@" for contact names and "@SCN@" for source contact names.
The dataset comprises a total of 46,562 samples, ensuring a balanced representation of utterance categories. For experimentation and model development, a partitioning strategy allocated 70% of the dataset for training, 15% for validation, and an additional 15% for testing purposes.
| Input | Output |
|---|---|
| can you remind @CN@ to meet the beautician | hi @CN@, @SCN@ reminds you to meet the beautician |
| tell @CN@ to select a spot for the picnic | hi @CN@, @SCN@ asks you to select a spot for the picnic |
| tell @CN@ to answer the call | hi @CN@, @SCN@ requested you to answer the call |
Sample rows from the dataset
The BLEU (BiLingual Evaluation Understudy) metric was adopted for the automatic evaluation of a machine-translated text. BLEU scores, ranging from 0 to 1, measure the similarity of machine-translated text to a set of high-quality reference translations. The BLEU metric provides a robust evaluation of model performance.
1. Snip Direct Messages:
The easiest strategy is just snipping the actual message from the text and passing just the message. For example, a text tells Bob, I'm late for dinner extracting the message I'm late for dinner and passing it on to the recipient. However, as easy as it sounds won't work for many sentences such as "Ask Bob, should I bring him dinner?" would be extracted as should I bring him dinner which doesn't sound natural at all, and is not something you will text to another person in normal conversations.
2. Rule-Based Approach:
Step 1: Classification Messages are classified into four types: AskWH, AskYN, Request, and Statement. A dataset of questions was curated and used to train a Naive Bayes Classifier and Gradient Boosted Tree Classifier, achieving an F1 score of 0.93.
Step 2: Identify Direct vs Indirect Questions POS tagging and dependency parsing were employed to identify direct questions from indirect ones.
Step 3: Transformation Changing word order for questions (AskWH and AskYN). Swapping pronouns to convert first person to third person and vice versa. Adjusting verbs to agree with the modified pronouns. Adding appropriate prepend statements based on the message type.
3. Seq2Seq Model (Encoder-Decoder):
A sequence-to-sequence (seq2seq) model is a neural network architecture designed for tasks involving sequential data, where an input sequence is transformed into an output sequence. In the context of POV conversion, this model is used to encode a source voice message and generate a target voice message with the adjusted point of view.
Encoder-Decoder Architecture: The seq2seq model comprises two main components: an encoder and a decoder. The encoder processes the input sequence (source voice message) and represents it in a fixed-size internal state. The decoder then takes this internal state and generates the output sequence (target voice message).
RNN (Recurrent Neural Network): Recurrent layers are employed in both the encoder and decoder to handle sequential dependencies. However, traditional RNNs face challenges in capturing long-range dependencies due to vanishing or exploding gradient problems.
Bahdanau Self-Attention: To address the limitations of traditional RNNs, Bahdanau attention mechanism is introduced. This attention mechanism allows the model to focus on different parts of the input sequence when generating each element of the output sequence. Bahdanau attention enhances the model's ability to capture context across various positions in the input sequence.
4. Transformers
Transformers, introduced by Vaswani et al. in the paper "Attention is All You Need," represent a paradigm shift in sequence-to-sequence modeling. This architecture relies on self-attention mechanisms, dispensing with the need for recurrent connections. The HuggingFace library's Transformers package provides easy access to a variety of pre-trained transformer models.
T5-base Model: T5 (Text-to-Text Transfer Transformer) is a transformer-based model that is pre-trained for a variety of natural language processing tasks. The "base" variant refers to a model with moderate size and capability. In the context of POV conversion, the T5 model is fine-tuned on the dataset, leveraging its powerful language understanding and generation capabilities.
Fine-tuning: Fine-tuning involves adapting a pre-trained model to a specific task or dataset. In this case, the T5-base model is fine-tuned on the POV conversion dataset. Fine-tuning allows the model to specialize its knowledge for the particular nuances and patterns present in the dataset.
HuggingFace Hub: After fine-tuning, the resulting model is uploaded to the HuggingFace Hub. The Hub serves as a repository for sharing and accessing pre-trained models, enabling other researchers and developers to utilize the fine-tuned POV conversion model for their applications.
These varied approaches encompass both traditional machine learning and cutting-edge deep learning techniques, allowing us to comprehensively assess the strengths and limitations of different models in addressing the unique challenges posed by LLM-generated content detection in the context of academic writing.
Rule-based Results The rule-based approach achieved a BLEU score of 40.72, indicating a moderate level of performance in POV conversion. BLEU scores range from 0 to 100, with higher scores indicating better alignment between the generated and reference texts. The breakdown of precision scores for different n-gram matches (unigrams, bigrams, trigrams, and 4-grams) reveals varying levels of accuracy, with higher precision for unigrams and lower precision for larger n-grams.
Seq2Seq Model (Encoder-Decoder) Results:
The seq2seq model, utilizing an RNN encoder-decoder architecture with Bahdanau self-attention, produced the following examples of POV conversion:
Original: "Can you ask cn to read the catalog,"
Translated: "Hi @CN@, @SCN@ wanted you to read the catalog."
Original: "Can you ask cn how to play a proper cover drive,"
Translated: "Hi @CN@, @SCN@ is eager to know how to play a proper cover drive."
Original: "Ask cn, what punishment would you give someone who is on welfare,"
Translated: "Hi @CN@, @SCN@ is asking what punishment would you give someone who is on welfare illegally?"
Transformers (T5-base Model) Results:
The T5-base model, fine-tuned on the dataset, achieved an evaluation BLEU score of 65.90. This score suggests a higher level of performance compared to the rule-based approach. BLEU scores above 60 are generally considered indicative of good performance in machine translation tasks.
The comparison table summarizes the BLEU scores for different experiments using distinct models. The T5-base model outperformed both the initial rule-based experiment and the BART model in terms of BLEU scores, indicating its effectiveness in POV conversion for the given dataset. The choice of model architecture plays a crucial role in achieving higher accuracy and naturalness in generated sequences.
| Model | Score (BLEU) | Original paper |
|---|---|---|
| Snipping messages | 37.2 | - |
| Rule-based | 40.72 | 46.6 |
| Encoder-Decoder | 64.21 | 55.7 |
| T5 | 66.425 | 63.8 |
Table 1: Results from different experiments
In the exploration of point-of-view (POV) conversion in voice assistant interactions, we employed a rule-based approach, a seq2seq model with an encoder-decoder architecture, and a Transformer model (T5-base) fine-tuned on the dataset. Each approach aimed to enhance the naturalness and context preservation of voice messages as they are relayed through virtual assistants. Here are the key takeaways:
1. Rule-Based Approach:
- Achieved a moderate BLEU score of 40.72.
- Provided a foundational understanding of the challenges and complexities in POV conversion.
- Demonstrated the importance of considering different n-gram matches for precision.
2. Seq2Seq Model (Encoder-Decoder):
- Presented a step towards more sophisticated models.
- Generated examples showed promising results in transforming voice messages while maintaining coherence.
3. Transformers (T5-base Model):
- Outperformed the rule-based approach and the BART model with a BLEU score of 65.90.
- Leveraged the power of pre-trained language models and fine-tuning for improved POV conversion.
- Demonstrated the effectiveness of Transformer architectures in handling complex language tasks.
Insights and Implications:
- Model Performance: The T5-base model showcased superior performance, emphasizing the efficacy of leveraging Transformer architectures for POV conversion tasks.
- Context Preservation: All approaches grappled with challenges in preserving context, especially in nuanced language and complex sentence structures.
- Future Directions: The study opens avenues for further research, including exploring advanced Transformer architectures, integrating contextual embeddings, and addressing challenges related to diverse language patterns.
- User Experience: Enhanced POV conversion contributes to a more natural and user-friendly interaction with voice assistants, paving the way for improved user experiences.
As voice assistants become integral to daily life, the ability to convert and convey messages effectively becomes crucial. The presented approaches provide a foundation for advancing the capabilities of virtual assistants in understanding and responding to user messages with improved context awareness and natural language processing. The success of the Transformer model suggests that state-of-the-art language models hold significant promise in addressing the intricacies of POV conversion in voice-based interactions.
@inproceedings{iglee2020,
author={Isabelle G. Lee and Vera Zu and Sai Srujana Buddi and Dennis Liang and Purva Kulkarni and Jack Fitzgerald},
title={{Converting the Point of View of Messages Spoken to Virtual Assistants}},
year=2020,
booktitle={Findings of EMNLP 2020},
doi={to-be-added},
url={to-be-added}
}
Lee, I.G., et al. "Converting the Point of View of Messages Spoken to Virtual Assistants"
Under CDLA-Sharing 1.0 License