Answer to Question 1-1


a. Word embeddings, such as word2vec, take context into account by learning vector representations of words based on the words that appear in the same context, such as sentences or paragraphs. The main difference between word embeddings and TF-IDF is that word embeddings capture the semantic meaning of words, while TF-IDF captures the frequency of words in a document relative to their frequency in a collection of documents. 





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Answer to Question 1-2


To segment the sentence "I love NLP a lot" into subwords using the given byte-pair-encoding (BPE) codes, we need to identify the longest substrings that can be formed by combining adjacent BPE codes.

Starting from the left, the first subword is "I", which is a single BPE code. The next subword is "I love", which is formed by combining the BPE codes "I" and "love". The next subword is "I love NLP", which is formed by combining the BPE codes "I love" and "NLP". The final subword is "I love NLP a lot", which is formed by combining the BPE codes "I love NLP" and "a lot".

Therefore, the segmented sentence is:

[0.000,0.194] "I"
[0.194,0.498] "I love"
[0.194,0.792] "I love NLP"
[0.194,0.988] "I love NLP a lot" 





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Answer to Question 1-3


a. The shape of the output projection is (10,000, 300).

b. It is possible that Bart's training pipeline is not broken. The context window size of 30, 40, and 50 words might not be large enough to capture the context of the words in the news headlines. Increasing the context window size could potentially improve the trained word vectors. However, without more information about the training pipeline, it is not possible to definitively conclude whether the pipeline is broken or not. 





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Answer to Question 1-4


a. True. Subword-based models can capture more complex morphological patterns than whole-word models, especially in morphologically-rich languages.

b. True. A unigram language model is a simple model that assumes that the probability of a word is proportional to its frequency in the corpus.

c. False. One-hot word representations are not suitable for measuring semantic similarity between words, as they do not capture the meaning of words.

d. True. In LDA, a document is modeled as a mixture of topics, where each topic is a distribution over words.

e. True. The term frequency in TF-IDF is multiplied by the inverse document frequency, which decreases the importance of words that occur frequently in the corpus.

f. False. In HMMs for part-of-speech tagging, the hidden states are not the words, but rather the part-of-speech tags that the words belong to. 





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Answer to Question 2-1


a. For the sentence classifier, a suitable model could be a simple neural network with a single hidden layer. The input would be the 300-dimensional word embedding vectors, and the output would be a 3-dimensional vector representing the classification labels (happy, neutral, sad). The intermediate operation would be a dot product between the input and the weights of the hidden layer, followed by an activation function (such as sigmoid or softmax). The parameter shapes would be:

* Input: 300-dimensional word embedding vectors
* Hidden layer: 3-dimensional weights (one for each classification label)
* Output: 3-dimensional vector representing the classification labels

b. The model from the previous subquestion is not suitable for the audio utterance classification task for two reasons:

1. The model is designed for text classification, and the input is spectrograms, which are different types of data. Spectrograms are 2D arrays representing the frequency content of audio signals, and they require different preprocessing and feature extraction techniques compared to text data.
2. The model does not take into account the temporal information in the audio signals, which is important for audio classification tasks.

c. An improved model for the audio utterance classification task could be a convolutional neural network (CNN) with multiple layers. The input would be the 80-dimensional spectrograms, and the output would be a 3-dimensional vector representing the classification labels (happy, neutral, sad). The intermediate operations would include convolutional layers, pooling layers, and fully connected layers. The parameter shapes would be:

* Input: 80-dimensional spectrograms
* Convolutional layers: multiple sets of filters with different sizes and strides
* Pooling layers: down-sampling the feature maps from the convolutional layers
* Fully connected layers: transforming the flattened feature maps into the output vector
* Output: 3-dimensional vector representing the classification labels 





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Answer to Question 2-2


a. The sequence classification approach is not optimal for dialog act identification because dialogs are inherently dynamic and context-dependent. In the example dialog, the patient's knee pain and swelling are related, but the doctor's response to the patient's initial complaint is not sufficient to capture the full context of the patient's condition. The model will certainly make a mistake if it relies solely on the input utterance without considering the previous utterances in the conversation.

b. The task of dialog act identification is a sequence labeling problem. The input is a sequence of utterances, and the output is a sequence of labels that correspond to each utterance. The sentence embedding dimension d is used to represent each utterance as a fixed-size vector, but the model needs to take into account the context of the previous utterances to accurately predict the label of each utterance.

c. A model for dialog act identification using sequence labeling can be implemented using a recurrent neural network (RNN) or a transformer-based model. The input is a sequence of sentence embeddings, where each embedding corresponds to an utterance in the conversation. The intermediate operation involves processing the input sequence through the RNN or transformer layers to capture the context of the previous utterances. The output is a sequence of labels that correspond to each utterance in the conversation. 





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Answer to Question 3-1


a. Autoregressive means that the Transformer decoder uses the output of the previous layer as input for the next layer. In the case of the Transformer decoder, the output of the previous layer is the encoder's output, which is then used as input for the decoder's self-attention layer. This is different from parallel processing, where all layers are processed simultaneously.

b. The Transformer decoder self-attention must be partially masked out during training to prevent the model from looking at future tokens in the sequence. This is because the decoder is trying to predict the next token in the sequence, and if it could see future tokens, it would have an unfair advantage.

c. The weights that should be masked out are the ones that correspond to future tokens in the sequence. For example, when the decoder is trying to predict the next token, it should not be able to look at tokens that come after it.

d. The attention weights when querying from the word "Mary" should be the same whether the sequence is "John loves Mary" or "Mary loves John". This is because the attention weights depend on the query and key vectors, which are the same regardless of the order of the sequence. 





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Answer to Question 3-2


a. Two solutions to adapt a summarization model trained on news to the medical domain when faced with unknown words are:

1. Expanding the vocabulary: One solution is to expand the vocabulary of the model to include medical terms. This can be done by either manually adding relevant medical terms to the vocabulary or by using a method like word embedding to generate embeddings for the medical terms and then adding these embeddings to the vocabulary.
2. Fine-tuning the model: Another solution is to fine-tune the model on a medical corpus. This involves training the model on a dataset of medical articles and adjusting the model's parameters to better capture the nuances of the medical domain.

b. ROUGE-n is based on the n-gram precision and recall. It measures the overlap between the generated summary and the reference summary in terms of n-grams.

c. The model receives high ROUGE-2 scores despite the non-grammatical output because ROUGE-2 only considers the presence of exact n-grams in the generated summary and the reference summary. It does not consider the grammaticality or coherence of the generated summary. To avoid this problem, we can use a different metric like BLEU, which takes into account the grammaticality and coherence of the generated summary.

To reduce the amount of repetition in the generated output, we can use a technique called "repetition suppression". This involves identifying repeated n-grams in the generated summary and replacing them with a single occurrence of the n-gram. This can be done using a simple algorithm that keeps track of the n-grams that have already been generated and replaces any repeated n-grams with the previously generated occurrence. 





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Answer to Question 3-3


Answer a:
An advantage of using BERT as an encoder with CTC for machine translation is that it leverages the pre-trained language model to capture the semantic meaning of the input text, which can improve the translation quality.

A disadvantage of this approach is that BERT is a text-based model and may not be able to capture the nuances of audio data, such as pronunciation and intonation, which are important for accurate machine translation.

Answer b:
To improve the performance of the model, one could consider incorporating additional audio features, such as prosody and pitch, into the input. This would allow the model to capture more information about the audio data, which could help to improve the translation accuracy. Additionally, fine-tuning the BERT model on a larger dataset of audio-text pairs could also help to improve the model's performance. 





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Answer to Question 3-4


For question a, a possible model for the text-to-SQL task could be a combination of natural language processing (NLP) and SQL parsing. The NLP component would process the question and extract the relevant information, such as the table name, column names, and the desired operation (e.g., SELECT, WHERE). The SQL parsing component would then use this information to generate the corresponding SQL query.

For question b, if the model encounters a question that is not answerable by the information in the table, it should return an error message indicating that the query is invalid. To adapt the model to handle unanswerable questions, you could add an additional step to the NLP component that checks if the question is answerable by the information in the table. If the question is not answerable, the model should return an error message. 





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Answer to Question 4-1


a. The advantage of this approach is that it allows for fine-tuning of the BERT model on a small dataset for named-entity recognition, while keeping the BERT parameters frozen. This means that the pre-trained BERT model is not overwritten, and the model can still benefit from the knowledge it has gained from previous tasks. The adapters in each layer allow for the learning of new information specific to the task at hand, without affecting the existing knowledge in the BERT model.

b. The adapters should be inserted after the attention and before the output in each layer of the BERT model. This is because the attention mechanism in the BERT model is responsible for capturing the relationships between different parts of the input sequence, and the output layer is responsible for producing the final output of the model. By inserting the adapters after the attention and before the output, the model can learn to transform the input sequence into a more suitable representation for the task at hand, without affecting the existing attention and output mechanisms.

c. To calculate the number of parameters added by inserting the adapters, we need to consider the number of parameters in each linear projection. Each linear projection has two linear layers, with the first projecting down to 256 dimensions and the second projecting up to the original dimension. The number of parameters in each linear layer is given by the formula n \* (in\_features \* out\_features), where n is the number of features in the input sequence. Assuming that the input sequence has 768 features, the number of parameters in each linear layer is 768 \* 256 = 199,680. Since there are two linear layers in each linear projection, the total number of parameters added by inserting the adapters is 199,680 \* 12 = 2,395,840. 





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Answer to Question 4-2


a. The DPR model uses the BERT CLS token to represent sentences, which is different from pooling the word vectors in the passages or questions. The BERT CLS token is a single token that represents the entire sentence, while pooling involves combining the word vectors to form a sentence representation. The advantages of using the BERT CLS token include:

1. The BERT CLS token captures the semantic meaning of the entire sentence, which is important for understanding the context of the question and the passage.
2. The BERT CLS token is trained on a large corpus of text, which allows it to capture a wide range of linguistic patterns and relationships.
3. The BERT CLS token is pre-trained, which means it can be fine-tuned for specific tasks, such as passage retrieval, without requiring additional training data.

b. Including irrelevant/negative pairs in the training objective is important because it helps the model learn to distinguish between relevant and irrelevant question-passage pairs. Without including negative pairs, the model might not be able to learn the differences between relevant and irrelevant pairs, which could lead to poor performance on the test set. 





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